{ "cells": [ { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "### Studen't Performance Prediction Model\n", "\n", "The model predicts student performance in weeks 3, 5, and 7 of a 16-week CS1 programming course. Starting from the grade, delivery time, and the number of attempts the student generates in programming labs and an exam. \n", "\n", "#### Data Dictionary\n", "\n", "| Variable | Type | Description |\n", "|---------------------------|------------------|-------------|\n", "| lab_1 | decimal | Grade laboratory 1 |\n", "| delivery_time_lab_1 | decimal | Delivery time laboratory 1 (days) |\n", "| attempts_lab_1 | integer | Number of attempts laboratory 1 |\n", "| lab_2 | decimal | Grade laboratory 2 |\n", "| lab_3 | decimal | Grade laboratory 3 |\n", "| grade | integer | Final grade (0- Low performance; 1- Medium performance; 2- High performance) |\n", "\n", "The performance threshold is:\n", "\n", "* 0 - Low performance (Final grade between 0.0 and 2.9)\n", "* 1 - Medium performance (Final grade between 3.0 and 4.0)\n", "* 2 - High performance (Final grade between 4.1 and 5.0)" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "#### Import the libraries" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# Import the libraries\n", "import numpy as np\n", "import pandas as pd\n", "import seaborn as sb\n", "import matplotlib.pyplot as plt\n", "\n", "# Algorithms of classification\n", "# Naive Bayes\n", "from sklearn.naive_bayes import GaussianNB\n", "# SVC\n", "from sklearn.svm import SVC\n", "# Decision Tree\n", "from sklearn.tree import DecisionTreeClassifier\n", "# Random Forest\n", "from sklearn.ensemble import RandomForestClassifier\n", "# Logistic Regression\n", "from sklearn.linear_model import LogisticRegression\n", "# K-NN\n", "from sklearn.neighbors import KNeighborsClassifier\n", "# MLP\n", "from sklearn.neural_network import MLPClassifier\n", "# Gradient Boosting\n", "from sklearn.ensemble import GradientBoostingClassifier\n", "\n", "# Import of library for data split\n", "from sklearn.model_selection import train_test_split\n", "# Confusion matrix\n", "from sklearn.metrics import confusion_matrix\n", "# Classification report\n", "from sklearn.metrics import classification_report\n", "# Import the libraries for the metrics\n", "from sklearn.metrics import precision_score, recall_score, f1_score\n", "\n", "# Library to calculate the mean and standard deviation used in the characteristics\n", "from sklearn.preprocessing import StandardScaler\n", "# Library Grid Search\n", "from sklearn.model_selection import GridSearchCV" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "### Load data to DataFrame" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/html": [ "
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lab_1delivery_time_lab_1attempts_lab_1lab_2lab_3grade
04.40.6324.44.92
13.30.3313.33.71
24.70.7834.34.82
34.30.2814.04.92
44.20.50103.14.91
.....................
4632.23.8963.03.51
4644.60.3510.00.00
4654.60.4815.00.01
4664.60.0000.00.00
4674.50.0000.00.00
\n", "

468 rows × 6 columns

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" ], "text/plain": [ " lab_1 delivery_time_lab_1 attempts_lab_1 lab_2 lab_3 grade\n", "0 4.4 0.63 2 4.4 4.9 2\n", "1 3.3 0.33 1 3.3 3.7 1\n", "2 4.7 0.78 3 4.3 4.8 2\n", "3 4.3 0.28 1 4.0 4.9 2\n", "4 4.2 0.50 10 3.1 4.9 1\n", ".. ... ... ... ... ... ...\n", "463 2.2 3.89 6 3.0 3.5 1\n", "464 4.6 0.35 1 0.0 0.0 0\n", "465 4.6 0.48 1 5.0 0.0 1\n", "466 4.6 0.00 0 0.0 0.0 0\n", "467 4.5 0.00 0 0.0 0.0 0\n", "\n", "[468 rows x 6 columns]" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Load data to DataFrame\n", "data = pd.read_csv(\"data/classification_data.csv\", sep=\";\")\n", "\n", "data" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "### Data preprocessing" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Column NaN\n", "lab_1 0\n", "delivery_time_lab_1 0\n", "attempts_lab_1 0\n", "lab_2 0\n", "lab_3 0\n", "grade 0\n", "dtype: int64\n", "(468, 6)\n" ] } ], "source": [ "# Find NaN records to delete\n", "print('Column NaN')\n", "print(data.isnull().sum(axis = 0))\n", "print(data.shape)\n", "\n", "#data = data.dropna()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "grade\n", "0 162\n", "1 200\n", "2 106\n", "dtype: int64" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Consult the number of records for qualification\n", "data.groupby('grade').size()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 200\n", "1 200\n", "2 200\n", "Name: grade, dtype: int64" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Resample\n", "from sklearn.utils import resample\n", "\n", "df_low = data[data['grade'] == 0]\n", "df_medium = data[data['grade'] == 1]\n", "df_high = data[data['grade'] == 2]\n", "\n", "data_resample_low = resample(df_low,\n", " replace = True,\n", " n_samples = 200,\n", " random_state = 1)\n", "\n", "data_resample_high = resample(df_high,\n", " replace = True,\n", " n_samples = 200,\n", " random_state = 1)\n", "\n", "data2 = pd.concat([data_resample_low, df_medium, data_resample_high])\n", "\n", "data2['grade'].value_counts()\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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lab_1delivery_time_lab_1attempts_lab_1lab_2lab_3grade
count600.000000600.000000600.000000600.00000600.000000600.000000
mean2.9540002.2029333.0600003.293504.0448331.000000
std1.8544224.2004924.5140921.835591.5908880.817178
min0.0000000.0000000.0000000.000000.0000000.000000
25%1.0000000.3000001.0000001.600004.0000000.000000
50%3.5500000.4800001.0000004.000004.9000001.000000
75%4.7000001.0000003.0000004.900005.0000002.000000
max5.00000022.08000039.0000005.000005.0000002.000000
\n", "
" ], "text/plain": [ " lab_1 delivery_time_lab_1 attempts_lab_1 lab_2 lab_3 \\\n", "count 600.000000 600.000000 600.000000 600.00000 600.000000 \n", "mean 2.954000 2.202933 3.060000 3.29350 4.044833 \n", "std 1.854422 4.200492 4.514092 1.83559 1.590888 \n", "min 0.000000 0.000000 0.000000 0.00000 0.000000 \n", "25% 1.000000 0.300000 1.000000 1.60000 4.000000 \n", "50% 3.550000 0.480000 1.000000 4.00000 4.900000 \n", "75% 4.700000 1.000000 3.000000 4.90000 5.000000 \n", "max 5.000000 22.080000 39.000000 5.00000 5.000000 \n", "\n", " grade \n", "count 600.000000 \n", "mean 1.000000 \n", "std 0.817178 \n", "min 0.000000 \n", "25% 0.000000 \n", "50% 1.000000 \n", "75% 2.000000 \n", "max 2.000000 " ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# DataFrame statistics\n", "data2.describe()" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "#### Training and testing set" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "# Features\t\n", "features = ['lab_1','delivery_time_lab_1','attempts_lab_1','lab_2', 'lab_3']\n", "X = data2[features]\n", "# Target variable\n", "y = data2['grade'].values\n", "\n", "# The data is split for training (80% training and 20% testing)\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, train_size = 0.8, random_state= 1)" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "#### Hide warnings" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "import warnings\n", "\n", "def fxn():\n", " warnings.warn(\"deprecated\", DeprecationWarning)\n", "\n", "with warnings.catch_warnings():\n", " warnings.simplefilter(\"ignore\")\n", " fxn()" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "#### Best Features - Eli5" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", "\n", "\n", "\n", " \n", "\n", " \n", "\n", " \n", "\n", " \n", "\n", " \n", "\n", " \n", "\n", "\n", " \n", "\n", " \n", "\n", " \n", "\n", " \n", "\n", " \n", "\n", " \n", "\n", "\n", " \n", "\n", " \n", "\n", " \n", "\n", " \n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
WeightFeature
\n", " 0.4232\n", " \n", " \n", " lab_1\n", "
\n", " 0.3835\n", " \n", " \n", " lab_2\n", "
\n", " 0.1292\n", " \n", " \n", " lab_3\n", "
\n", " 0.0360\n", " \n", " \n", " delivery_time_lab_1\n", "
\n", " 0.0281\n", " \n", " \n", " attempts_lab_1\n", "
\n", " \n", "\n", " \n", "\n", "\n", " \n", "\n", " \n", "\n", " \n", "\n", " \n", "\n", " \n", "\n", " \n", " \n", "
\n", " 
lab_1 <= 4.050  (59.8%)\n",
       "    lab_2 <= 2.150  (20.2%)\n",
       "        lab_1 <= 3.650  (17.9%)\n",
       "            lab_3 <= 4.700  (13.1%)  ---> [1.000, 0.000, 0.000]\n",
       "            lab_3 > 4.700  (4.8%)\n",
       "                delivery_time_lab_1 <= 0.315  (0.8%)\n",
       "                    lab_2 <= 0.650  (0.6%)  ---> [1.000, 0.000, 0.000]\n",
       "                    lab_2 > 0.650  (0.2%)  ---> [0.000, 1.000, 0.000]\n",
       "                delivery_time_lab_1 > 0.315  (4.0%)  ---> [1.000, 0.000, 0.000]\n",
       "        lab_1 > 3.650  (2.3%)\n",
       "            lab_3 <= 3.250  (1.0%)  ---> [1.000, 0.000, 0.000]\n",
       "            lab_3 > 3.250  (1.2%)\n",
       "                lab_2 <= 0.650  (0.4%)\n",
       "                    delivery_time_lab_1 <= 0.940  (0.2%)  ---> [1.000, 0.000, 0.000]\n",
       "                    delivery_time_lab_1 > 0.940  (0.2%)  ---> [0.000, 1.000, 0.000]\n",
       "                lab_2 > 0.650  (0.8%)  ---> [0.000, 1.000, 0.000]\n",
       "    lab_2 > 2.150  (39.6%)\n",
       "        lab_1 <= 0.150  (10.2%)\n",
       "            lab_2 <= 3.950  (3.3%)  ---> [1.000, 0.000, 0.000]\n",
       "            lab_2 > 3.950  (6.9%)\n",
       "                delivery_time_lab_1 <= 9.640  (5.8%)\n",
       "                    lab_2 <= 4.550  (0.4%)  ---> [0.000, 1.000, 0.000]\n",
       "                    lab_2 > 4.550  (5.4%)\n",
       "                        attempts_lab_1 <= 8.000  (4.6%)\n",
       "                            lab_3 <= 4.950  (1.5%)\n",
       "                                attempts_lab_1 <= 2.500  (1.2%)  ---> [1.000, 0.000, 0.000]\n",
       "                                attempts_lab_1 > 2.500  (0.2%)  ---> [0.000, 1.000, 0.000]\n",
       "                            lab_3 > 4.950  (3.1%)\n",
       "                                delivery_time_lab_1 <= 0.275  (0.4%)  ---> [1.000, 0.000, 0.000]\n",
       "                                delivery_time_lab_1 > 0.275  (2.7%)\n",
       "                                    delivery_time_lab_1 <= 0.450  (0.6%)  ---> [0.000, 1.000, 0.000]\n",
       "                                    delivery_time_lab_1 > 0.450  (2.1%)\n",
       "                                        delivery_time_lab_1 <= 6.555  (1.9%)\n",
       "                                            attempts_lab_1 <= 5.500  (1.7%)\n",
       "                                                delivery_time_lab_1 <= 0.605  (0.6%)  ---> [1.000, 0.000, 0.000]\n",
       "                                                delivery_time_lab_1 > 0.605  (1.0%)\n",
       "                                                    delivery_time_lab_1 <= 3.835  (0.6%)\n",
       "                                                        lab_2 <= 4.800  (0.4%)\n",
       "                                                            attempts_lab_1 <= 2.500  (0.2%)  ---> [0.000, 1.000, 0.000]\n",
       "                                                            attempts_lab_1 > 2.500  (0.2%)  ---> [1.000, 0.000, 0.000]\n",
       "                                                        lab_2 > 4.800  (0.2%)  ---> [0.000, 1.000, 0.000]\n",
       "                                                    delivery_time_lab_1 > 3.835  (0.4%)  ---> [1.000, 0.000, 0.000]\n",
       "                                            attempts_lab_1 > 5.500  (0.2%)  ---> [0.000, 1.000, 0.000]\n",
       "                                        delivery_time_lab_1 > 6.555  (0.2%)  ---> [0.000, 1.000, 0.000]\n",
       "                        attempts_lab_1 > 8.000  (0.8%)  ---> [1.000, 0.000, 0.000]\n",
       "                delivery_time_lab_1 > 9.640  (1.0%)\n",
       "                    attempts_lab_1 <= 11.000  (0.8%)  ---> [0.000, 1.000, 0.000]\n",
       "                    attempts_lab_1 > 11.000  (0.2%)  ---> [1.000, 0.000, 0.000]\n",
       "        lab_1 > 0.150  (29.4%)\n",
       "            lab_3 <= 2.750  (0.8%)  ---> [1.000, 0.000, 0.000]\n",
       "            lab_3 > 2.750  (28.5%)\n",
       "                lab_1 <= 3.350  (21.5%)\n",
       "                    lab_2 <= 3.450  (4.0%)\n",
       "                        lab_1 <= 1.350  (1.9%)\n",
       "                            attempts_lab_1 <= 3.500  (1.7%)  ---> [1.000, 0.000, 0.000]\n",
       "                            attempts_lab_1 > 3.500  (0.2%)  ---> [0.000, 1.000, 0.000]\n",
       "                        lab_1 > 1.350  (2.1%)  ---> [0.000, 1.000, 0.000]\n",
       "                    lab_2 > 3.450  (17.5%)\n",
       "                        delivery_time_lab_1 <= 0.265  (2.7%)\n",
       "                            lab_2 <= 4.700  (1.5%)  ---> [0.000, 1.000, 0.000]\n",
       "                            lab_2 > 4.700  (1.2%)\n",
       "                                lab_3 <= 4.950  (0.4%)  ---> [0.000, 0.000, 1.000]\n",
       "                                lab_3 > 4.950  (0.8%)  ---> [0.000, 1.000, 0.000]\n",
       "                        delivery_time_lab_1 > 0.265  (14.8%)\n",
       "                            lab_3 <= 3.350  (0.8%)\n",
       "                                delivery_time_lab_1 <= 0.725  (0.2%)  ---> [1.000, 0.000, 0.000]\n",
       "                                delivery_time_lab_1 > 0.725  (0.6%)  ---> [0.000, 1.000, 0.000]\n",
       "                            lab_3 > 3.350  (14.0%)  ---> [0.000, 1.000, 0.000]\n",
       "                lab_1 > 3.350  (7.1%)\n",
       "                    lab_1 <= 3.550  (2.9%)\n",
       "                        lab_2 <= 3.950  (0.6%)  ---> [0.000, 1.000, 0.000]\n",
       "                        lab_2 > 3.950  (2.3%)  ---> [0.000, 0.000, 1.000]\n",
       "                    lab_1 > 3.550  (4.2%)  ---> [0.000, 1.000, 0.000]\n",
       "lab_1 > 4.050  (40.2%)\n",
       "    lab_2 <= 2.650  (9.4%)\n",
       "        lab_3 <= 2.800  (4.4%)\n",
       "            lab_2 <= 2.050  (4.2%)\n",
       "                lab_1 <= 4.750  (3.1%)  ---> [1.000, 0.000, 0.000]\n",
       "                lab_1 > 4.750  (1.0%)\n",
       "                    lab_2 <= 0.550  (0.8%)  ---> [1.000, 0.000, 0.000]\n",
       "                    lab_2 > 0.550  (0.2%)  ---> [0.000, 1.000, 0.000]\n",
       "            lab_2 > 2.050  (0.2%)  ---> [0.000, 1.000, 0.000]\n",
       "        lab_3 > 2.800  (5.0%)\n",
       "            lab_1 <= 4.950  (4.8%)  ---> [0.000, 1.000, 0.000]\n",
       "            lab_1 > 4.950  (0.2%)  ---> [0.000, 0.000, 1.000]\n",
       "    lab_2 > 2.650  (30.8%)\n",
       "        lab_3 <= 2.750  (1.2%)\n",
       "            lab_2 <= 3.300  (0.2%)  ---> [1.000, 0.000, 0.000]\n",
       "            lab_2 > 3.300  (1.0%)  ---> [0.000, 1.000, 0.000]\n",
       "        lab_3 > 2.750  (29.6%)\n",
       "            lab_2 <= 3.200  (2.5%)\n",
       "                lab_1 <= 4.650  (0.6%)\n",
       "                    delivery_time_lab_1 <= 0.575  (0.2%)  ---> [0.000, 0.000, 1.000]\n",
       "                    delivery_time_lab_1 > 0.575  (0.4%)  ---> [0.000, 1.000, 0.000]\n",
       "                lab_1 > 4.650  (1.9%)  ---> [0.000, 0.000, 1.000]\n",
       "            lab_2 > 3.200  (27.1%)\n",
       "                lab_3 <= 3.950  (2.7%)\n",
       "                    lab_1 <= 4.650  (0.4%)\n",
       "                        attempts_lab_1 <= 5.000  (0.2%)  ---> [0.000, 0.000, 1.000]\n",
       "                        attempts_lab_1 > 5.000  (0.2%)  ---> [0.000, 1.000, 0.000]\n",
       "                    lab_1 > 4.650  (2.3%)  ---> [0.000, 0.000, 1.000]\n",
       "                lab_3 > 3.950  (24.4%)  ---> [0.000, 0.000, 1.000]
\n", " \n", "\n", "\n", "\n" ], "text/plain": [ "" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Model\n", "dtc = DecisionTreeClassifier() \n", " \n", "# The model is trained\n", "dtc.fit(X_train, y_train)\n", "\n", "pred = dtc.predict(X_test)\n", "\n", "# Best Features - Eli5\n", "from eli5 import show_weights\n", "\n", "show_weights(dtc, feature_names = features)" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "### --------------------------------------------------------------------------\n", "\n", "### Prediction with hyperparameter (Grid Search)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Naive Bayes" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Fitting 10 folds for each of 100 candidates, totalling 1000 fits\n", "Best Parameters (GridSearch): GaussianNB(var_smoothing=0.04328761281083057)\n", "-----------------------------------------------------------\n", "[[29 12 0]\n", " [10 19 3]\n", " [ 0 4 43]]\n", " precision recall f1-score support\n", "\n", " 0 0.74 0.71 0.72 41\n", " 1 0.54 0.59 0.57 32\n", " 2 0.93 0.91 0.92 47\n", "\n", " accuracy 0.76 120\n", " macro avg 0.74 0.74 0.74 120\n", "weighted avg 0.76 0.76 0.76 120\n", "\n", "Precisión: 0.76\n", "Recall: 0.76\n", "F1-Score: 0.76\n" ] } ], "source": [ "scaler = StandardScaler()\n", "X_train = scaler.fit_transform(X_train)\n", "X_test = scaler.transform(X_test)\n", "\n", "nb = GaussianNB()\n", "\n", "# Parameters\n", "grid = {\n", " 'var_smoothing': np.logspace(0,-9, num=100)\n", "}\n", "\n", "grid_search = GridSearchCV(estimator = nb, \n", " param_grid = grid, \n", " cv= 10, \n", " verbose=1,\n", " n_jobs=-1, \n", " scoring = \"accuracy\")\n", "\n", "searchResults = grid_search.fit(X_train, y_train.ravel())\n", "\n", "# Extract the best model and evaluate it\n", "bestModel = searchResults.best_estimator_\n", "\n", "print(\"Best Parameters (GridSearch):\", bestModel)\n", "print(\"-----------------------------------------------------------\")\n", "\n", "# Object best hyperparameters\n", "nb = bestModel\n", "\n", "# Train the model with the best hyperparameters\n", "nb.fit(X_train, y_train)\n", "\n", "pred = nb.predict(X_test)\n", "\n", "# Confusion matrix\n", "print(confusion_matrix(y_test, pred))\n", "# Classification report\n", "print(classification_report(y_test, pred))\n", "\n", "# Metrics: Precision, Recall, F1-Score\n", "print(\"Precision: \", round(precision_score(y_test, pred, average='weighted'), 2))\n", "print(\"Recall: \", round(recall_score(y_test, pred, average='weighted'),2))\n", "print(\"F1-Score: \", round(f1_score(y_test, pred, average='weighted'),2))\n", "\n" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "#### SVC" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Fitting 10 folds for each of 32 candidates, totalling 320 fits\n", "Best Parameters (GridSearch): SVC(C=100, gamma=0.1)\n", "-----------------------------------------------------------\n", "[[39 2 0]\n", " [ 2 30 0]\n", " [ 0 3 44]]\n", " precision recall f1-score support\n", "\n", " 0 0.95 0.95 0.95 41\n", " 1 0.86 0.94 0.90 32\n", " 2 1.00 0.94 0.97 47\n", "\n", " accuracy 0.94 120\n", " macro avg 0.94 0.94 0.94 120\n", "weighted avg 0.95 0.94 0.94 120\n", "\n", "Precisión: 0.95\n", "Recall: 0.94\n", "F1-Score: 0.94\n" ] } ], "source": [ "scaler = StandardScaler()\n", "X_train = scaler.fit_transform(X_train)\n", "X_test = scaler.transform(X_test)\n", "\n", "svm = SVC()\n", "\n", "# Parameters\n", "gamma = [0.1, 1.0, 10, 100]\n", "C = [0.1, 1.0, 10, 100]\n", "kernel = ['rbf','linear']\n", "\n", "grid = dict(gamma = gamma,\n", " C = C,\n", " kernel = kernel)\n", "\n", "grid_search = GridSearchCV(estimator = svm, \n", " param_grid = grid, \n", " cv= 10, \n", " verbose=1, \n", " n_jobs=-1,\n", " scoring = \"accuracy\")\n", "\n", "searchResults = grid_search.fit(X_train, y_train.ravel())\n", "\n", "# Extract the best model and evaluate it\n", "bestModel = searchResults.best_estimator_\n", "\n", "print(\"Best Parameters (GridSearch):\", bestModel)\n", "print(\"-----------------------------------------------------------\")\n", "\n", "# Object best hyperparameters\n", "svm = bestModel\n", " \n", "# Train the model with the best hyperparameters\n", "svm.fit(X_train, y_train)\n", "\n", "pred = svm.predict(X_test)\n", "\n", "# Confusion matrix\n", "print(confusion_matrix(y_test, pred))\n", "# Classification report\n", "print(classification_report(y_test, pred))\n", "\n", "# Metrics: Precision, Recall, F1-Score\n", "print(\"Precision: \", round(precision_score(y_test, pred, average='weighted'), 2))\n", "print(\"Recall: \", round(recall_score(y_test, pred, average='weighted'),2))\n", "print(\"F1-Score: \", round(f1_score(y_test, pred, average='weighted'),2))" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "#### Decision Tree" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Fitting 10 folds for each of 50 candidates, totalling 500 fits\n", "Best Parameters (GridSearch): DecisionTreeClassifier(criterion='entropy', max_depth=10, min_samples_leaf=5)\n", "-----------------------------------------------------------\n", "[[33 8 0]\n", " [ 3 29 0]\n", " [ 0 2 45]]\n", " precision recall f1-score support\n", "\n", " 0 0.92 0.80 0.86 41\n", " 1 0.74 0.91 0.82 32\n", " 2 1.00 0.96 0.98 47\n", "\n", " accuracy 0.89 120\n", " macro avg 0.89 0.89 0.88 120\n", "weighted avg 0.90 0.89 0.89 120\n", "\n", "Precisión: 0.9\n", "Recall: 0.89\n", "F1-Score: 0.89\n" ] } ], "source": [ "scaler = StandardScaler()\n", "X_train = scaler.fit_transform(X_train)\n", "X_test = scaler.transform(X_test)\n", "\n", "dt = DecisionTreeClassifier()\n", "\n", "# Parameters\n", "max_depth = [2, 3, 5, 10, 20]\n", "min_samples_leaf = [5, 10, 20, 50, 100]\n", "criterion = [\"gini\", \"entropy\"]\n", "\n", "grid = dict(max_depth = max_depth,\n", " min_samples_leaf = min_samples_leaf,\n", " criterion = criterion)\n", "\n", "grid_search = GridSearchCV(estimator = dt, \n", " param_grid = grid, \n", " cv= 10, \n", " verbose=1, \n", " n_jobs=-1,\n", " scoring = \"accuracy\")\n", "\n", "searchResults = grid_search.fit(X_train, y_train.ravel())\n", "\n", "# Extract the best model and evaluate it\n", "bestModel = searchResults.best_estimator_\n", "\n", "print(\"Best Parameters (GridSearch):\", bestModel)\n", "print(\"-----------------------------------------------------------\")\n", "\n", "# Object best hyperparameters\n", "dtc = bestModel\n", " \n", "# Train the model with the best hyperparameters\n", "dtc.fit(X_train, y_train)\n", "\n", "pred = dtc.predict(X_test)\n", "\n", "# Confusion matrix\n", "print(confusion_matrix(y_test, pred))\n", "# Classification report\n", "print(classification_report(y_test, pred))\n", "\n", "# Metrics: Precision, Recall, F1-Score\n", "print(\"Precision: \", round(precision_score(y_test, pred, average='weighted'), 2))\n", "print(\"Recall: \", round(recall_score(y_test, pred, average='weighted'),2))\n", "print(\"F1-Score: \", round(f1_score(y_test, pred, average='weighted'),2))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Random Forest" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Fitting 10 folds for each of 1080 candidates, totalling 10800 fits\n", "Best Parameters (GridSearch): RandomForestClassifier(max_depth=10, max_features=None, n_estimators=20)\n", "-----------------------------------------------------------\n", "[[39 2 0]\n", " [ 3 28 1]\n", " [ 0 0 47]]\n", " precision recall f1-score support\n", "\n", " 0 0.93 0.95 0.94 41\n", " 1 0.93 0.88 0.90 32\n", " 2 0.98 1.00 0.99 47\n", "\n", " accuracy 0.95 120\n", " macro avg 0.95 0.94 0.94 120\n", "weighted avg 0.95 0.95 0.95 120\n", "\n", "Precision: 0.95\n", "Recall: 0.95\n", "F1-Score: 0.95\n" ] } ], "source": [ "scaler = StandardScaler()\n", "X_train = scaler.fit_transform(X_train)\n", "X_test = scaler.transform(X_test)\n", "\n", "rf = RandomForestClassifier()\n", "\n", "# Parameters\n", "bootstrap = [True, False]\n", "max_depth = [10, 20, 50, 100, None]\n", "max_features = ['sqrt', 'log2', None]\n", "min_samples_leaf = [1, 2, 4]\n", "min_samples_split = [2, 5, 10]\n", "n_estimators = [5, 20, 50, 100]\n", "\n", "grid = dict(bootstrap = bootstrap, \n", " max_depth = max_depth,\n", " max_features = max_features,\n", " min_samples_leaf = min_samples_leaf,\n", " min_samples_split = min_samples_split,\n", " n_estimators = n_estimators)\n", "\n", "grid_search = GridSearchCV(estimator = rf, \n", " param_grid = grid, \n", " cv= 10, \n", " verbose=1, \n", " n_jobs=-1,\n", " scoring = \"accuracy\")\n", "\n", "searchResults = grid_search.fit(X_train, y_train.ravel())\n", "\n", "# Extract the best model and evaluate it\n", "bestModel = searchResults.best_estimator_\n", "\n", "print(\"Best Parameters (GridSearch):\", bestModel)\n", "print(\"-----------------------------------------------------------\")\n", "\n", "# Object best hyperparameters\n", "rf = bestModel\n", " \n", "# Train the model with the best hyperparameters\n", "rf.fit(X_train, y_train)\n", "\n", "pred = rf.predict(X_test)\n", "\n", "# Confusion matrix\n", "print(confusion_matrix(y_test, pred))\n", "# Classification report\n", "print(classification_report(y_test, pred))\n", "\n", "# Metrics: Precision, Recall, F1-Score\n", "print(\"Precision: \", round(precision_score(y_test, pred, average='weighted'), 2))\n", "print(\"Recall: \", round(recall_score(y_test, pred, average='weighted'),2))\n", "print(\"F1-Score: \", round(f1_score(y_test, pred, average='weighted'),2))" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "#### Logistic Regression" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Fitting 10 folds for each of 60 candidates, totalling 600 fits\n", "Best Parameters (GridSearch): LogisticRegression(C=100)\n", "-----------------------------------------------------------\n", "[[31 10 0]\n", " [ 1 30 1]\n", " [ 0 0 47]]\n", " precision recall f1-score support\n", "\n", " 0 0.97 0.76 0.85 41\n", " 1 0.75 0.94 0.83 32\n", " 2 0.98 1.00 0.99 47\n", "\n", " accuracy 0.90 120\n", " macro avg 0.90 0.90 0.89 120\n", "weighted avg 0.91 0.90 0.90 120\n", "\n", "Precision: 0.91\n", "Recall: 0.9\n", "F1-Score: 0.9\n" ] } ], "source": [ "scaler = StandardScaler()\n", "X_train = scaler.fit_transform(X_train)\n", "X_test = scaler.transform(X_test)\n", "\n", "lr = LogisticRegression()\n", "\n", "# Parameters\n", "solver = ['lbfgs','newton-cg','liblinear']\n", "penalty = ['l2']\n", "C = [100, 10, 1.0, 0.1, 0.01]\n", "max_iter = [100, 1000,2500, 5000]\n", "\n", "grid = dict(solver = solver,\n", " penalty = penalty,\n", " C = C,\n", " max_iter = max_iter)\n", "\n", "grid_search = GridSearchCV(estimator = lr, \n", " param_grid = grid, \n", " cv= 10, \n", " verbose=1, \n", " n_jobs=-1,\n", " scoring = \"accuracy\")\n", "\n", "searchResults = grid_search.fit(X_train, y_train.ravel())\n", "\n", "# Extract the best model and evaluate it\n", "bestModel = searchResults.best_estimator_\n", "\n", "print(\"Best Parameters (GridSearch):\", bestModel)\n", "print(\"-----------------------------------------------------------\")\n", "\n", "# Object best hyperparameters\n", "lr = bestModel\n", " \n", "# Train the model with the best hyperparameters\n", "lr.fit(X_train, y_train)\n", "\n", "pred = lr.predict(X_test)\n", "\n", "# Confusion matrix\n", "print(confusion_matrix(y_test, pred))\n", "# Classification report\n", "print(classification_report(y_test, pred))\n", "\n", "# Metrics: Precision, Recall, F1-Score\n", "print(\"Precision: \", round(precision_score(y_test, pred, average='weighted'), 2))\n", "print(\"Recall: \", round(recall_score(y_test, pred, average='weighted'),2))\n", "print(\"F1-Score: \", round(f1_score(y_test, pred, average='weighted'),2))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### K-NN" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Fitting 10 folds for each of 32 candidates, totalling 320 fits\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n", " mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Best Parameters (GridSearch): KNeighborsClassifier(n_neighbors=1)\n", "-----------------------------------------------------------\n", "[[37 4 0]\n", " [ 2 29 1]\n", " [ 0 1 46]]\n", " precision recall f1-score support\n", "\n", " 0 0.95 0.90 0.92 41\n", " 1 0.85 0.91 0.88 32\n", " 2 0.98 0.98 0.98 47\n", "\n", " accuracy 0.93 120\n", " macro avg 0.93 0.93 0.93 120\n", "weighted avg 0.93 0.93 0.93 120\n", "\n", "Precision: 0.93\n", "Recall: 0.93\n", "F1-Score: 0.93\n" ] } ], "source": [ "scaler = StandardScaler()\n", "X_train = scaler.fit_transform(X_train)\n", "X_test = scaler.transform(X_test)\n", "\n", "knn = KNeighborsClassifier()\n", "\n", "# Parameters\n", "n_neighbors = [1, 3, 5, 10]\n", "weights = ['uniform','distance']\n", "algorithm = ['auto','ball_tree','kd_tree','brute']\n", "\n", "grid = dict(n_neighbors = n_neighbors,\n", " weights = weights,\n", " algorithm = algorithm)\n", "\n", "grid_search = GridSearchCV(estimator = knn, \n", " param_grid = grid, \n", " cv= 10, \n", " verbose=1, \n", " n_jobs=-1,\n", " scoring = \"accuracy\")\n", "\n", "searchResults = grid_search.fit(X_train, y_train.ravel())\n", "\n", "# Extract the best model and evaluate it\n", "bestModel = searchResults.best_estimator_\n", "\n", "print(\"Best Parameters (GridSearch):\", bestModel)\n", "print(\"-----------------------------------------------------------\")\n", "\n", "# Object best hyperparameters\n", "lr = bestModel\n", " \n", "# Train the model with the best hyperparameters\n", "lr.fit(X_train, y_train)\n", "\n", "pred = lr.predict(X_test)\n", "\n", "# Confusion matrix\n", "print(confusion_matrix(y_test, pred))\n", "# Classification report\n", "print(classification_report(y_test, pred))\n", "\n", "# Metrics: Precision, Recall, F1-Score\n", "print(\"Precision: \", round(precision_score(y_test, pred, average='weighted'), 2))\n", "print(\"Recall: \", round(recall_score(y_test, pred, average='weighted'),2))\n", "print(\"F1-Score: \", round(f1_score(y_test, pred, average='weighted'),2))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### MLP" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Fitting 10 folds for each of 64 candidates, totalling 640 fits\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Best Parameters (GridSearch): MLPClassifier(activation='tanh', hidden_layer_sizes=(50,), max_iter=150)\n", "-----------------------------------------------------------\n", "[[31 10 0]\n", " [ 2 29 1]\n", " [ 0 0 47]]\n", " precision recall f1-score support\n", "\n", " 0 0.94 0.76 0.84 41\n", " 1 0.74 0.91 0.82 32\n", " 2 0.98 1.00 0.99 47\n", "\n", " accuracy 0.89 120\n", " macro avg 0.89 0.89 0.88 120\n", "weighted avg 0.90 0.89 0.89 120\n", "\n", "Precision: 0.9\n", "Recall: 0.89\n", "F1-Score: 0.89\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (150) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n" ] } ], "source": [ "scaler = StandardScaler()\n", "X_train = scaler.fit_transform(X_train)\n", "X_test = scaler.transform(X_test)\n", "\n", "mlp = MLPClassifier(max_iter=150)\n", "\n", "# Parameters\n", "hidden_layer_sizes = [(10,),(20,),(50,),(100,)]\n", "activation = ['tanh', 'relu']\n", "solver = ['sgd', 'adam']\n", "alpha = [0.0001, 0.05]\n", "learning_rate = ['constant','adaptive']\n", "\n", "grid = dict(hidden_layer_sizes = hidden_layer_sizes,\n", " activation = activation, \n", " solver = solver,\n", " alpha = alpha,\n", " learning_rate = learning_rate)\n", "\n", "grid_search = GridSearchCV(estimator = mlp, \n", " param_grid = grid, \n", " cv= 10, \n", " verbose=1, \n", " n_jobs=-1,\n", " scoring = \"accuracy\")\n", "\n", "searchResults = grid_search.fit(X_train, y_train.ravel())\n", "\n", "# Extract the best model and evaluate it\n", "bestModel = searchResults.best_estimator_\n", "\n", "print(\"Best Parameters (GridSearch):\", bestModel)\n", "print(\"-----------------------------------------------------------\")\n", "\n", "# Object best hyperparameters\n", "lr = bestModel\n", " \n", "# Train the model with the best hyperparameters\n", "lr.fit(X_train, y_train)\n", "\n", "pred = lr.predict(X_test)\n", "\n", "# Confusion matrix\n", "print(confusion_matrix(y_test, pred))\n", "# Classification report\n", "print(classification_report(y_test, pred))\n", "\n", "# Metrics: Precision, Recall, F1-Score\n", "print(\"Precision: \", round(precision_score(y_test, pred, average='weighted'), 2))\n", "print(\"Recall: \", round(recall_score(y_test, pred, average='weighted'),2))\n", "print(\"F1-Score: \", round(f1_score(y_test, pred, average='weighted'),2))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### GBC" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Fitting 10 folds for each of 60 candidates, totalling 600 fits\n", "Best Parameters (GridSearch): GradientBoostingClassifier(learning_rate=0.2, max_features='sqrt')\n", "-----------------------------------------------------------\n", "[[38 3 0]\n", " [ 2 30 0]\n", " [ 0 0 47]]\n", " precision recall f1-score support\n", "\n", " 0 0.95 0.93 0.94 41\n", " 1 0.91 0.94 0.92 32\n", " 2 1.00 1.00 1.00 47\n", "\n", " accuracy 0.96 120\n", " macro avg 0.95 0.95 0.95 120\n", "weighted avg 0.96 0.96 0.96 120\n", "\n", "Precision: 0.96\n", "Recall: 0.96\n", "F1-Score: 0.96\n" ] } ], "source": [ "scaler = StandardScaler()\n", "X_train = scaler.fit_transform(X_train)\n", "X_test = scaler.transform(X_test)\n", "\n", "gbc = GradientBoostingClassifier()\n", "\n", "# Parameters\n", "learning_rate = [0.01, 0.05, 0.1, 0.15, 0.2]\n", "criterion = ['friedman_mse', 'squared_error']\n", "max_depth = [3,5,8]\n", "max_features = ['log2','sqrt']\n", "\n", "grid = dict(learning_rate = learning_rate,\n", " criterion = criterion,\n", " max_depth = max_depth,\n", " max_features = max_features)\n", "\n", "grid_search = GridSearchCV(estimator = gbc, \n", " param_grid = grid, \n", " cv= 10, \n", " verbose=1, \n", " n_jobs=-1,\n", " scoring = \"accuracy\")\n", "\n", "searchResults = grid_search.fit(X_train, y_train.ravel())\n", "\n", "# Extract the best model and evaluate it\n", "bestModel = searchResults.best_estimator_\n", "\n", "print(\"Best Parameters (GridSearch):\", bestModel)\n", "print(\"-----------------------------------------------------------\")\n", "\n", "# Object best hyperparameters\n", "gbc = bestModel\n", " \n", "# Train the model with the best hyperparameters\n", "gbc.fit(X_train, y_train)\n", "\n", "pred = gbc.predict(X_test)\n", "\n", "# Confusion matrix\n", "print(confusion_matrix(y_test, pred))\n", "# Classification report\n", "print(classification_report(y_test, pred))\n", "\n", "# Metrics: Precision, Recall, F1-Score\n", "print(\"Precision: \", round(precision_score(y_test, pred, average='weighted'), 2))\n", "print(\"Recall: \", round(recall_score(y_test, pred, average='weighted'),2))\n", "print(\"F1-Score: \", round(f1_score(y_test, pred, average='weighted'),2))" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3.9.13 64-bit", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.13" }, "vscode": { "interpreter": { "hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49" } } }, "nbformat": 4, "nbformat_minor": 4 }