diff --git a/ml_rn.py b/ml_rn.py
index 37f4dfb6e8cb9a0ef2e9538565162cfaa11085c6..08198cbdf6132d00562c5770deed46abb62cd033 100644
--- a/ml_rn.py
+++ b/ml_rn.py
@@ -1,70 +1,73 @@
import numpy as np
import pandas as pd
+import time
from sklearn.model_selection import train_test_split, GridSearchCV
-from sklearn.preprocessing import StandardScaler
+from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
-import time
from sklearn.neural_network import MLPRegressor
def load_and_describe_data(file_path):
"""
- Charge un fichier CSV.
- :param file_path: Chemin du fichier CSV
- :return: DataFrame Pandas
+ Charge un fichier CSV et affiche les informations de base.
"""
df = pd.read_csv(file_path)
-
-
+ print(df.info())
return df
-df = load_and_describe_data('data_sup_0popularity.csv')
-print(df.info())
-
def train_mlp(df):
- # 1. Séparation des features et de la cible
+ start_time = time.time() # ⏳ Timer
+
+ # 1️⃣ Séparation des features et de la cible
X = df.drop(columns=["popularity", "id", "artists", "name", "release_date", "date_sortie", "duration_ms", "nom_artiste"])
- y = df['popularity']
+ y = df["popularity"]
- # 2. Séparation train/test
+ # 2️⃣ Split train/test
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
- # 3. Normalisation des features
- scaler = StandardScaler()
+ # 3️⃣ Normalisation des features
+ scaler = MinMaxScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)
- # 4. Définition des hyperparamètres à tester
+ # 4️⃣ Définition des hyperparamètres
param_grid = {
- 'hidden_layer_sizes': [(50,), (100,), (100, 50), (100, 100)],
- 'activation': ['relu', 'tanh'],
- 'solver': ['adam', 'sgd'],
- 'learning_rate_init': [0.001, 0.01, 0.1],
- 'max_iter': [500]
+ "hidden_layer_sizes": [(50,), (100,), (100, 50), (100, 100)],
+ "activation": ["relu", "tanh"],
+ "solver": ["adam", "sgd"],
+ "learning_rate_init": [0.001, 0.01, 0.1],
+ "max_iter": [500],
+ "early_stopping": [True] # Arrête si la validation ne s'améliore pas
}
- # 5. Recherche des meilleurs hyperparamètres avec GridSearchCV
+ # 5️⃣ Recherche des meilleurs hyperparamètres
mlp = MLPRegressor(random_state=42)
- grid_search = GridSearchCV(mlp, param_grid, cv=3, scoring='r2', verbose=2)
+ grid_search = GridSearchCV(mlp, param_grid, cv=3, scoring="r2", verbose=2)
grid_search.fit(X_train_scaled, y_train)
- # 6. Affichage des meilleurs paramètres
- print("Meilleurs paramètres :", grid_search.best_params_)
+ # 6️⃣ Affichage des meilleurs paramètres
+ best_params = grid_search.best_params_
+ print("\n✅ Meilleurs paramètres :", best_params)
- # 7. Prédictions avec le meilleur modèle
+ # 7️⃣ Prédiction avec le meilleur modèle
best_mlp = grid_search.best_estimator_
y_pred = best_mlp.predict(X_test_scaled)
- # 8. Évaluation du modèle
+ # 8️⃣ Évaluation du modèle
mae = mean_absolute_error(y_test, y_pred)
rmse = np.sqrt(mean_squared_error(y_test, y_pred))
r2 = r2_score(y_test, y_pred)
- print(f"📊 MLPRegressor Optimisé - MAE: {mae:.2f}, RMSE: {rmse:.2f}, R²: {r2:.3f}")
+ print(f"\n📊 MLPRegressor - MAE: {mae:.2f}, RMSE: {rmse:.2f}, R²: {r2:.3f}")
+
+ # 9️⃣ Ajout des prédictions au DataFrame
+ df.loc[X_test.index, "pred_mlp"] = y_pred
- # 9. Ajout des prédictions au DataFrame original
- df.loc[X_test.index, 'pred_mlp'] = y_pred
- print(df.head(40))
+ # ⏳ Temps d'exécution
+ elapsed_time = time.time() - start_time
+ print(f"\n⏱️ Temps d'exécution : {elapsed_time:.2f} secondes")
return df
-train_mlp(df)
+# 📂 Chargement et entraînement
+df = load_and_describe_data("data_sup_0popularity.csv")
+df = train_mlp(df)
diff --git a/model_rn.txt b/model_rn.txt
deleted file mode 100644
index 861ae8004af3cc49ac830d93a15e2f2040d16705..0000000000000000000000000000000000000000
--- a/model_rn.txt
+++ /dev/null
@@ -1,495 +0,0 @@
-nohup: ignoring input
-<class 'pandas.core.frame.DataFrame'>
-RangeIndex: 98159 entries, 0 to 98158
-Data columns (total 27 columns):
- # Column Non-Null Count Dtype
---- ------ -------------- -----
- 0 id 98159 non-null object
- 1 artists 98159 non-null object
- 2 name 98159 non-null object
- 3 year 98159 non-null int64
- 4 acousticness 98159 non-null float64
- 5 danceability 98159 non-null float64
- 6 duration_ms 98159 non-null int64
- 7 energy 98159 non-null float64
- 8 explicit 98159 non-null int64
- 9 instrumentalness 98159 non-null float64
- 10 key 98159 non-null int64
- 11 liveness 98159 non-null float64
- 12 loudness 98159 non-null float64
- 13 mode 98159 non-null int64
- 14 release_date 98159 non-null object
- 15 speechiness 98159 non-null float64
- 16 tempo 98159 non-null float64
- 17 valence 98159 non-null float64
- 18 popularity 98159 non-null int64
- 19 date_sortie 98159 non-null object
- 20 nom_artiste 98159 non-null object
- 21 nb_caracteres_sans_espaces 98159 non-null int64
- 22 nb_artistes 98159 non-null int64
- 23 featuring 98159 non-null int64
- 24 duree_minute 98159 non-null float64
- 25 categorie_annee 98159 non-null int64
- 26 categorie_tempo 98159 non-null int64
-dtypes: float64(10), int64(11), object(6)
-memory usage: 20.2+ MB
-None
-Fitting 3 folds for each of 48 candidates, totalling 144 fits
-[CV] END activation=relu, hidden_layer_sizes=(50,), learning_rate_init=0.001, max_iter=500, solver=adam; total time= 1.1min
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
- warnings.warn(
-[CV] END activation=relu, hidden_layer_sizes=(50,), learning_rate_init=0.001, max_iter=500, solver=adam; total time= 1.2min
-[CV] END activation=relu, hidden_layer_sizes=(50,), learning_rate_init=0.001, max_iter=500, solver=adam; total time= 1.0min
-[CV] END activation=relu, hidden_layer_sizes=(50,), learning_rate_init=0.001, max_iter=500, solver=sgd; total time= 17.4s
-[CV] END activation=relu, hidden_layer_sizes=(50,), learning_rate_init=0.001, max_iter=500, solver=sgd; total time= 17.1s
-[CV] END activation=relu, hidden_layer_sizes=(50,), learning_rate_init=0.001, max_iter=500, solver=sgd; total time= 16.3s
-[CV] END activation=relu, hidden_layer_sizes=(50,), learning_rate_init=0.01, max_iter=500, solver=adam; total time= 21.4s
-[CV] END activation=relu, hidden_layer_sizes=(50,), learning_rate_init=0.01, max_iter=500, solver=adam; total time= 18.5s
-[CV] END activation=relu, hidden_layer_sizes=(50,), learning_rate_init=0.01, max_iter=500, solver=adam; total time= 23.3s
-[CV] END activation=relu, hidden_layer_sizes=(50,), learning_rate_init=0.01, max_iter=500, solver=sgd; total time= 7.2s
-[CV] END activation=relu, hidden_layer_sizes=(50,), learning_rate_init=0.01, max_iter=500, solver=sgd; total time= 5.2s
-[CV] END activation=relu, hidden_layer_sizes=(50,), learning_rate_init=0.01, max_iter=500, solver=sgd; total time= 11.2s
-[CV] END activation=relu, hidden_layer_sizes=(50,), learning_rate_init=0.1, max_iter=500, solver=adam; total time= 10.8s
-[CV] END activation=relu, hidden_layer_sizes=(50,), learning_rate_init=0.1, max_iter=500, solver=adam; total time= 7.5s
-[CV] END activation=relu, hidden_layer_sizes=(50,), learning_rate_init=0.1, max_iter=500, solver=adam; total time= 10.8s
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_base.py:172: RuntimeWarning: overflow encountered in square
- return ((y_true - y_pred) ** 2).mean() / 2
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul
- ret = a @ b
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul
- ret = a @ b
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/numpy/_core/_methods.py:135: RuntimeWarning: overflow encountered in reduce
- ret = umr_sum(arr, axis, dtype, out, keepdims, where=where)
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
- warnings.warn(
-[CV] END activation=relu, hidden_layer_sizes=(50,), learning_rate_init=0.1, max_iter=500, solver=sgd; total time= 47.4s
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_base.py:172: RuntimeWarning: overflow encountered in square
- return ((y_true - y_pred) ** 2).mean() / 2
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul
- ret = a @ b
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul
- ret = a @ b
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
- warnings.warn(
-[CV] END activation=relu, hidden_layer_sizes=(50,), learning_rate_init=0.1, max_iter=500, solver=sgd; total time= 47.7s
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_base.py:172: RuntimeWarning: overflow encountered in square
- return ((y_true - y_pred) ** 2).mean() / 2
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul
- ret = a @ b
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul
- ret = a @ b
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
- warnings.warn(
-[CV] END activation=relu, hidden_layer_sizes=(50,), learning_rate_init=0.1, max_iter=500, solver=sgd; total time= 47.5s
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
- warnings.warn(
-[CV] END activation=relu, hidden_layer_sizes=(100,), learning_rate_init=0.001, max_iter=500, solver=adam; total time= 1.7min
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
- warnings.warn(
-[CV] END activation=relu, hidden_layer_sizes=(100,), learning_rate_init=0.001, max_iter=500, solver=adam; total time= 1.8min
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
- warnings.warn(
-[CV] END activation=relu, hidden_layer_sizes=(100,), learning_rate_init=0.001, max_iter=500, solver=adam; total time= 1.8min
-[CV] END activation=relu, hidden_layer_sizes=(100,), learning_rate_init=0.001, max_iter=500, solver=sgd; total time= 46.5s
-[CV] END activation=relu, hidden_layer_sizes=(100,), learning_rate_init=0.001, max_iter=500, solver=sgd; total time= 48.7s
-[CV] END activation=relu, hidden_layer_sizes=(100,), learning_rate_init=0.001, max_iter=500, solver=sgd; total time= 33.3s
-[CV] END activation=relu, hidden_layer_sizes=(100,), learning_rate_init=0.01, max_iter=500, solver=adam; total time= 47.7s
-[CV] END activation=relu, hidden_layer_sizes=(100,), learning_rate_init=0.01, max_iter=500, solver=adam; total time= 37.4s
-[CV] END activation=relu, hidden_layer_sizes=(100,), learning_rate_init=0.01, max_iter=500, solver=adam; total time= 30.2s
-[CV] END activation=relu, hidden_layer_sizes=(100,), learning_rate_init=0.01, max_iter=500, solver=sgd; total time= 10.5s
-[CV] END activation=relu, hidden_layer_sizes=(100,), learning_rate_init=0.01, max_iter=500, solver=sgd; total time= 9.5s
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul
- ret = a @ b
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_base.py:172: RuntimeWarning: overflow encountered in square
- return ((y_true - y_pred) ** 2).mean() / 2
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul
- ret = a @ b
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
- warnings.warn(
-[CV] END activation=relu, hidden_layer_sizes=(100,), learning_rate_init=0.01, max_iter=500, solver=sgd; total time= 1.1min
-[CV] END activation=relu, hidden_layer_sizes=(100,), learning_rate_init=0.1, max_iter=500, solver=adam; total time= 13.9s
-[CV] END activation=relu, hidden_layer_sizes=(100,), learning_rate_init=0.1, max_iter=500, solver=adam; total time= 12.6s
-[CV] END activation=relu, hidden_layer_sizes=(100,), learning_rate_init=0.1, max_iter=500, solver=adam; total time= 10.1s
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_base.py:172: RuntimeWarning: overflow encountered in square
- return ((y_true - y_pred) ** 2).mean() / 2
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul
- ret = a @ b
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul
- ret = a @ b
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
- warnings.warn(
-[CV] END activation=relu, hidden_layer_sizes=(100,), learning_rate_init=0.1, max_iter=500, solver=sgd; total time= 1.0min
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_base.py:172: RuntimeWarning: overflow encountered in square
- return ((y_true - y_pred) ** 2).mean() / 2
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul
- ret = a @ b
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul
- ret = a @ b
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
- warnings.warn(
-[CV] END activation=relu, hidden_layer_sizes=(100,), learning_rate_init=0.1, max_iter=500, solver=sgd; total time= 1.1min
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_base.py:172: RuntimeWarning: overflow encountered in square
- return ((y_true - y_pred) ** 2).mean() / 2
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul
- ret = a @ b
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul
- ret = a @ b
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
- warnings.warn(
-[CV] END activation=relu, hidden_layer_sizes=(100,), learning_rate_init=0.1, max_iter=500, solver=sgd; total time= 1.1min
-[CV] END activation=relu, hidden_layer_sizes=(100, 50), learning_rate_init=0.001, max_iter=500, solver=adam; total time= 2.6min
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
- warnings.warn(
-[CV] END activation=relu, hidden_layer_sizes=(100, 50), learning_rate_init=0.001, max_iter=500, solver=adam; total time= 4.6min
-[CV] END activation=relu, hidden_layer_sizes=(100, 50), learning_rate_init=0.001, max_iter=500, solver=adam; total time= 2.2min
-[CV] END activation=relu, hidden_layer_sizes=(100, 50), learning_rate_init=0.001, max_iter=500, solver=sgd; total time= 1.5min
-[CV] END activation=relu, hidden_layer_sizes=(100, 50), learning_rate_init=0.001, max_iter=500, solver=sgd; total time= 1.4min
-[CV] END activation=relu, hidden_layer_sizes=(100, 50), learning_rate_init=0.001, max_iter=500, solver=sgd; total time= 1.7min
-[CV] END activation=relu, hidden_layer_sizes=(100, 50), learning_rate_init=0.01, max_iter=500, solver=adam; total time= 4.4min
-[CV] END activation=relu, hidden_layer_sizes=(100, 50), learning_rate_init=0.01, max_iter=500, solver=adam; total time= 2.8min
-[CV] END activation=relu, hidden_layer_sizes=(100, 50), learning_rate_init=0.01, max_iter=500, solver=adam; total time= 4.7min
-[CV] END activation=relu, hidden_layer_sizes=(100, 50), learning_rate_init=0.01, max_iter=500, solver=sgd; total time= 4.7s
-[CV] END activation=relu, hidden_layer_sizes=(100, 50), learning_rate_init=0.01, max_iter=500, solver=sgd; total time= 18.3s
-[CV] END activation=relu, hidden_layer_sizes=(100, 50), learning_rate_init=0.01, max_iter=500, solver=sgd; total time= 3.8s
-[CV] END activation=relu, hidden_layer_sizes=(100, 50), learning_rate_init=0.1, max_iter=500, solver=adam; total time= 17.5s
-[CV] END activation=relu, hidden_layer_sizes=(100, 50), learning_rate_init=0.1, max_iter=500, solver=adam; total time= 24.4s
-[CV] END activation=relu, hidden_layer_sizes=(100, 50), learning_rate_init=0.1, max_iter=500, solver=adam; total time= 23.9s
-[CV] END activation=relu, hidden_layer_sizes=(100, 50), learning_rate_init=0.1, max_iter=500, solver=sgd; total time= 3.7s
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul
- ret = a @ b
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
- warnings.warn(
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul
- ret = a @ b
-[CV] END activation=relu, hidden_layer_sizes=(100, 50), learning_rate_init=0.1, max_iter=500, solver=sgd; total time= 2.5min
-[CV] END activation=relu, hidden_layer_sizes=(100, 50), learning_rate_init=0.1, max_iter=500, solver=sgd; total time= 3.6s
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
- warnings.warn(
-[CV] END activation=relu, hidden_layer_sizes=(100, 100), learning_rate_init=0.001, max_iter=500, solver=adam; total time= 4.6min
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
- warnings.warn(
-[CV] END activation=relu, hidden_layer_sizes=(100, 100), learning_rate_init=0.001, max_iter=500, solver=adam; total time= 4.7min
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
- warnings.warn(
-[CV] END activation=relu, hidden_layer_sizes=(100, 100), learning_rate_init=0.001, max_iter=500, solver=adam; total time= 4.7min
-[CV] END activation=relu, hidden_layer_sizes=(100, 100), learning_rate_init=0.001, max_iter=500, solver=sgd; total time= 2.8min
-[CV] END activation=relu, hidden_layer_sizes=(100, 100), learning_rate_init=0.001, max_iter=500, solver=sgd; total time= 3.3min
-[CV] END activation=relu, hidden_layer_sizes=(100, 100), learning_rate_init=0.001, max_iter=500, solver=sgd; total time= 3.2min
-[CV] END activation=relu, hidden_layer_sizes=(100, 100), learning_rate_init=0.01, max_iter=500, solver=adam; total time= 4.1min
-[CV] END activation=relu, hidden_layer_sizes=(100, 100), learning_rate_init=0.01, max_iter=500, solver=adam; total time= 4.1min
-[CV] END activation=relu, hidden_layer_sizes=(100, 100), learning_rate_init=0.01, max_iter=500, solver=adam; total time= 4.2min
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_base.py:172: RuntimeWarning: overflow encountered in square
- return ((y_true - y_pred) ** 2).mean() / 2
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul
- ret = a @ b
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul
- ret = a @ b
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
- warnings.warn(
-[CV] END activation=relu, hidden_layer_sizes=(100, 100), learning_rate_init=0.01, max_iter=500, solver=sgd; total time= 2.6min
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_base.py:172: RuntimeWarning: overflow encountered in square
- return ((y_true - y_pred) ** 2).mean() / 2
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul
- ret = a @ b
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul
- ret = a @ b
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
- warnings.warn(
-[CV] END activation=relu, hidden_layer_sizes=(100, 100), learning_rate_init=0.01, max_iter=500, solver=sgd; total time= 2.6min
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul
- ret = a @ b
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul
- ret = a @ b
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
- warnings.warn(
-[CV] END activation=relu, hidden_layer_sizes=(100, 100), learning_rate_init=0.01, max_iter=500, solver=sgd; total time= 2.7min
-[CV] END activation=relu, hidden_layer_sizes=(100, 100), learning_rate_init=0.1, max_iter=500, solver=adam; total time= 29.3s
-[CV] END activation=relu, hidden_layer_sizes=(100, 100), learning_rate_init=0.1, max_iter=500, solver=adam; total time= 22.4s
-[CV] END activation=relu, hidden_layer_sizes=(100, 100), learning_rate_init=0.1, max_iter=500, solver=adam; total time= 34.5s
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul
- ret = a @ b
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_base.py:172: RuntimeWarning: overflow encountered in square
- return ((y_true - y_pred) ** 2).mean() / 2
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul
- ret = a @ b
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
- warnings.warn(
-[CV] END activation=relu, hidden_layer_sizes=(100, 100), learning_rate_init=0.1, max_iter=500, solver=sgd; total time= 2.7min
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul
- ret = a @ b
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_base.py:172: RuntimeWarning: overflow encountered in square
- return ((y_true - y_pred) ** 2).mean() / 2
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul
- ret = a @ b
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
- warnings.warn(
-[CV] END activation=relu, hidden_layer_sizes=(100, 100), learning_rate_init=0.1, max_iter=500, solver=sgd; total time= 2.7min
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_base.py:172: RuntimeWarning: overflow encountered in square
- return ((y_true - y_pred) ** 2).mean() / 2
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul
- ret = a @ b
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: invalid value encountered in matmul
- ret = a @ b
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
- warnings.warn(
-[CV] END activation=relu, hidden_layer_sizes=(100, 100), learning_rate_init=0.1, max_iter=500, solver=sgd; total time= 2.7min
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
- warnings.warn(
-[CV] END activation=tanh, hidden_layer_sizes=(50,), learning_rate_init=0.001, max_iter=500, solver=adam; total time= 1.0min
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
- warnings.warn(
-[CV] END activation=tanh, hidden_layer_sizes=(50,), learning_rate_init=0.001, max_iter=500, solver=adam; total time= 1.0min
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
- warnings.warn(
-[CV] END activation=tanh, hidden_layer_sizes=(50,), learning_rate_init=0.001, max_iter=500, solver=adam; total time= 1.0min
-[CV] END activation=tanh, hidden_layer_sizes=(50,), learning_rate_init=0.001, max_iter=500, solver=sgd; total time= 17.2s
-[CV] END activation=tanh, hidden_layer_sizes=(50,), learning_rate_init=0.001, max_iter=500, solver=sgd; total time= 18.8s
-[CV] END activation=tanh, hidden_layer_sizes=(50,), learning_rate_init=0.001, max_iter=500, solver=sgd; total time= 22.6s
-[CV] END activation=tanh, hidden_layer_sizes=(50,), learning_rate_init=0.01, max_iter=500, solver=adam; total time= 27.3s
-[CV] END activation=tanh, hidden_layer_sizes=(50,), learning_rate_init=0.01, max_iter=500, solver=adam; total time= 31.2s
-[CV] END activation=tanh, hidden_layer_sizes=(50,), learning_rate_init=0.01, max_iter=500, solver=adam; total time= 18.1s
-[CV] END activation=tanh, hidden_layer_sizes=(50,), learning_rate_init=0.01, max_iter=500, solver=sgd; total time= 15.6s
-[CV] END activation=tanh, hidden_layer_sizes=(50,), learning_rate_init=0.01, max_iter=500, solver=sgd; total time= 12.6s
-[CV] END activation=tanh, hidden_layer_sizes=(50,), learning_rate_init=0.01, max_iter=500, solver=sgd; total time= 8.8s
-[CV] END activation=tanh, hidden_layer_sizes=(50,), learning_rate_init=0.1, max_iter=500, solver=adam; total time= 11.4s
-[CV] END activation=tanh, hidden_layer_sizes=(50,), learning_rate_init=0.1, max_iter=500, solver=adam; total time= 14.0s
-[CV] END activation=tanh, hidden_layer_sizes=(50,), learning_rate_init=0.1, max_iter=500, solver=adam; total time= 11.5s
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
- warnings.warn(
-[CV] END activation=tanh, hidden_layer_sizes=(50,), learning_rate_init=0.1, max_iter=500, solver=sgd; total time= 55.4s
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
- warnings.warn(
-[CV] END activation=tanh, hidden_layer_sizes=(50,), learning_rate_init=0.1, max_iter=500, solver=sgd; total time= 55.2s
-[CV] END activation=tanh, hidden_layer_sizes=(50,), learning_rate_init=0.1, max_iter=500, solver=sgd; total time= 1.5s
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
- warnings.warn(
-[CV] END activation=tanh, hidden_layer_sizes=(100,), learning_rate_init=0.001, max_iter=500, solver=adam; total time= 1.4min
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
- warnings.warn(
-[CV] END activation=tanh, hidden_layer_sizes=(100,), learning_rate_init=0.001, max_iter=500, solver=adam; total time= 1.4min
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
- warnings.warn(
-[CV] END activation=tanh, hidden_layer_sizes=(100,), learning_rate_init=0.001, max_iter=500, solver=adam; total time= 1.4min
-[CV] END activation=tanh, hidden_layer_sizes=(100,), learning_rate_init=0.001, max_iter=500, solver=sgd; total time= 48.8s
-[CV] END activation=tanh, hidden_layer_sizes=(100,), learning_rate_init=0.001, max_iter=500, solver=sgd; total time= 25.3s
-[CV] END activation=tanh, hidden_layer_sizes=(100,), learning_rate_init=0.001, max_iter=500, solver=sgd; total time= 53.5s
-[CV] END activation=tanh, hidden_layer_sizes=(100,), learning_rate_init=0.01, max_iter=500, solver=adam; total time= 31.3s
-[CV] END activation=tanh, hidden_layer_sizes=(100,), learning_rate_init=0.01, max_iter=500, solver=adam; total time= 45.3s
-[CV] END activation=tanh, hidden_layer_sizes=(100,), learning_rate_init=0.01, max_iter=500, solver=adam; total time= 37.0s
-[CV] END activation=tanh, hidden_layer_sizes=(100,), learning_rate_init=0.01, max_iter=500, solver=sgd; total time= 32.3s
-[CV] END activation=tanh, hidden_layer_sizes=(100,), learning_rate_init=0.01, max_iter=500, solver=sgd; total time= 15.0s
-[CV] END activation=tanh, hidden_layer_sizes=(100,), learning_rate_init=0.01, max_iter=500, solver=sgd; total time= 25.6s
-[CV] END activation=tanh, hidden_layer_sizes=(100,), learning_rate_init=0.1, max_iter=500, solver=adam; total time= 15.6s
-[CV] END activation=tanh, hidden_layer_sizes=(100,), learning_rate_init=0.1, max_iter=500, solver=adam; total time= 12.9s
-[CV] END activation=tanh, hidden_layer_sizes=(100,), learning_rate_init=0.1, max_iter=500, solver=adam; total time= 17.1s
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/numpy/_core/_methods.py:135: RuntimeWarning: overflow encountered in reduce
- ret = umr_sum(arr, axis, dtype, out, keepdims, where=where)
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_base.py:172: RuntimeWarning: overflow encountered in square
- return ((y_true - y_pred) ** 2).mean() / 2
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul
- ret = a @ b
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_base.py:127: RuntimeWarning: invalid value encountered in multiply
- delta *= 1 - Z**2
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
- warnings.warn(
-[CV] END activation=tanh, hidden_layer_sizes=(100,), learning_rate_init=0.1, max_iter=500, solver=sgd; total time= 1.3min
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/numpy/_core/_methods.py:135: RuntimeWarning: overflow encountered in reduce
- ret = umr_sum(arr, axis, dtype, out, keepdims, where=where)
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_base.py:172: RuntimeWarning: overflow encountered in square
- return ((y_true - y_pred) ** 2).mean() / 2
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul
- ret = a @ b
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_base.py:127: RuntimeWarning: invalid value encountered in multiply
- delta *= 1 - Z**2
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
- warnings.warn(
-[CV] END activation=tanh, hidden_layer_sizes=(100,), learning_rate_init=0.1, max_iter=500, solver=sgd; total time= 1.3min
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/numpy/_core/_methods.py:135: RuntimeWarning: overflow encountered in reduce
- ret = umr_sum(arr, axis, dtype, out, keepdims, where=where)
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_base.py:172: RuntimeWarning: overflow encountered in square
- return ((y_true - y_pred) ** 2).mean() / 2
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul
- ret = a @ b
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_base.py:127: RuntimeWarning: invalid value encountered in multiply
- delta *= 1 - Z**2
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
- warnings.warn(
-[CV] END activation=tanh, hidden_layer_sizes=(100,), learning_rate_init=0.1, max_iter=500, solver=sgd; total time= 1.3min
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
- warnings.warn(
-[CV] END activation=tanh, hidden_layer_sizes=(100, 50), learning_rate_init=0.001, max_iter=500, solver=adam; total time= 2.6min
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
- warnings.warn(
-[CV] END activation=tanh, hidden_layer_sizes=(100, 50), learning_rate_init=0.001, max_iter=500, solver=adam; total time= 2.8min
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
- warnings.warn(
-[CV] END activation=tanh, hidden_layer_sizes=(100, 50), learning_rate_init=0.001, max_iter=500, solver=adam; total time= 2.6min
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
- warnings.warn(
-[CV] END activation=tanh, hidden_layer_sizes=(100, 50), learning_rate_init=0.001, max_iter=500, solver=sgd; total time= 2.5min
-[CV] END activation=tanh, hidden_layer_sizes=(100, 50), learning_rate_init=0.001, max_iter=500, solver=sgd; total time= 1.7min
-[CV] END activation=tanh, hidden_layer_sizes=(100, 50), learning_rate_init=0.001, max_iter=500, solver=sgd; total time= 1.7min
-[CV] END activation=tanh, hidden_layer_sizes=(100, 50), learning_rate_init=0.01, max_iter=500, solver=adam; total time= 1.0min
-[CV] END activation=tanh, hidden_layer_sizes=(100, 50), learning_rate_init=0.01, max_iter=500, solver=adam; total time= 1.2min
-[CV] END activation=tanh, hidden_layer_sizes=(100, 50), learning_rate_init=0.01, max_iter=500, solver=adam; total time= 1.1min
-[CV] END activation=tanh, hidden_layer_sizes=(100, 50), learning_rate_init=0.01, max_iter=500, solver=sgd; total time= 32.0s
-[CV] END activation=tanh, hidden_layer_sizes=(100, 50), learning_rate_init=0.01, max_iter=500, solver=sgd; total time= 17.4s
-[CV] END activation=tanh, hidden_layer_sizes=(100, 50), learning_rate_init=0.01, max_iter=500, solver=sgd; total time= 24.2s
-[CV] END activation=tanh, hidden_layer_sizes=(100, 50), learning_rate_init=0.1, max_iter=500, solver=adam; total time= 18.0s
-[CV] END activation=tanh, hidden_layer_sizes=(100, 50), learning_rate_init=0.1, max_iter=500, solver=adam; total time= 20.1s
-[CV] END activation=tanh, hidden_layer_sizes=(100, 50), learning_rate_init=0.1, max_iter=500, solver=adam; total time= 6.8s
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/numpy/_core/_methods.py:135: RuntimeWarning: overflow encountered in reduce
- ret = umr_sum(arr, axis, dtype, out, keepdims, where=where)
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_base.py:172: RuntimeWarning: overflow encountered in square
- return ((y_true - y_pred) ** 2).mean() / 2
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul
- ret = a @ b
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_base.py:127: RuntimeWarning: invalid value encountered in multiply
- delta *= 1 - Z**2
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
- warnings.warn(
-[CV] END activation=tanh, hidden_layer_sizes=(100, 50), learning_rate_init=0.1, max_iter=500, solver=sgd; total time= 2.4min
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/numpy/_core/_methods.py:135: RuntimeWarning: overflow encountered in reduce
- ret = umr_sum(arr, axis, dtype, out, keepdims, where=where)
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_base.py:172: RuntimeWarning: overflow encountered in square
- return ((y_true - y_pred) ** 2).mean() / 2
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul
- ret = a @ b
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_base.py:127: RuntimeWarning: invalid value encountered in multiply
- delta *= 1 - Z**2
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
- warnings.warn(
-[CV] END activation=tanh, hidden_layer_sizes=(100, 50), learning_rate_init=0.1, max_iter=500, solver=sgd; total time= 2.4min
-[CV] END activation=tanh, hidden_layer_sizes=(100, 50), learning_rate_init=0.1, max_iter=500, solver=sgd; total time= 3.6s
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
- warnings.warn(
-[CV] END activation=tanh, hidden_layer_sizes=(100, 100), learning_rate_init=0.001, max_iter=500, solver=adam; total time= 3.5min
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
- warnings.warn(
-[CV] END activation=tanh, hidden_layer_sizes=(100, 100), learning_rate_init=0.001, max_iter=500, solver=adam; total time= 3.5min
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
- warnings.warn(
-[CV] END activation=tanh, hidden_layer_sizes=(100, 100), learning_rate_init=0.001, max_iter=500, solver=adam; total time= 3.5min
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
- warnings.warn(
-[CV] END activation=tanh, hidden_layer_sizes=(100, 100), learning_rate_init=0.001, max_iter=500, solver=sgd; total time= 3.1min
-[CV] END activation=tanh, hidden_layer_sizes=(100, 100), learning_rate_init=0.001, max_iter=500, solver=sgd; total time= 2.7min
-[CV] END activation=tanh, hidden_layer_sizes=(100, 100), learning_rate_init=0.001, max_iter=500, solver=sgd; total time= 2.6min
-[CV] END activation=tanh, hidden_layer_sizes=(100, 100), learning_rate_init=0.01, max_iter=500, solver=adam; total time= 1.4min
-[CV] END activation=tanh, hidden_layer_sizes=(100, 100), learning_rate_init=0.01, max_iter=500, solver=adam; total time= 1.6min
-[CV] END activation=tanh, hidden_layer_sizes=(100, 100), learning_rate_init=0.01, max_iter=500, solver=adam; total time= 1.3min
-[CV] END activation=tanh, hidden_layer_sizes=(100, 100), learning_rate_init=0.01, max_iter=500, solver=sgd; total time= 46.0s
-[CV] END activation=tanh, hidden_layer_sizes=(100, 100), learning_rate_init=0.01, max_iter=500, solver=sgd; total time= 52.7s
-[CV] END activation=tanh, hidden_layer_sizes=(100, 100), learning_rate_init=0.01, max_iter=500, solver=sgd; total time= 1.2min
-[CV] END activation=tanh, hidden_layer_sizes=(100, 100), learning_rate_init=0.1, max_iter=500, solver=adam; total time= 13.7s
-[CV] END activation=tanh, hidden_layer_sizes=(100, 100), learning_rate_init=0.1, max_iter=500, solver=adam; total time= 11.2s
-[CV] END activation=tanh, hidden_layer_sizes=(100, 100), learning_rate_init=0.1, max_iter=500, solver=adam; total time= 23.5s
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/numpy/_core/_methods.py:135: RuntimeWarning: overflow encountered in reduce
- ret = umr_sum(arr, axis, dtype, out, keepdims, where=where)
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_base.py:172: RuntimeWarning: overflow encountered in square
- return ((y_true - y_pred) ** 2).mean() / 2
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul
- ret = a @ b
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_base.py:127: RuntimeWarning: invalid value encountered in multiply
- delta *= 1 - Z**2
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
- warnings.warn(
-[CV] END activation=tanh, hidden_layer_sizes=(100, 100), learning_rate_init=0.1, max_iter=500, solver=sgd; total time= 3.0min
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/numpy/_core/_methods.py:135: RuntimeWarning: overflow encountered in reduce
- ret = umr_sum(arr, axis, dtype, out, keepdims, where=where)
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_base.py:172: RuntimeWarning: overflow encountered in square
- return ((y_true - y_pred) ** 2).mean() / 2
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul
- ret = a @ b
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_base.py:127: RuntimeWarning: invalid value encountered in multiply
- delta *= 1 - Z**2
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
- warnings.warn(
-[CV] END activation=tanh, hidden_layer_sizes=(100, 100), learning_rate_init=0.1, max_iter=500, solver=sgd; total time= 3.0min
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/numpy/_core/_methods.py:135: RuntimeWarning: overflow encountered in reduce
- ret = umr_sum(arr, axis, dtype, out, keepdims, where=where)
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_base.py:172: RuntimeWarning: overflow encountered in square
- return ((y_true - y_pred) ** 2).mean() / 2
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul
- ret = a @ b
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_base.py:127: RuntimeWarning: invalid value encountered in multiply
- delta *= 1 - Z**2
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
- warnings.warn(
-[CV] END activation=tanh, hidden_layer_sizes=(100, 100), learning_rate_init=0.1, max_iter=500, solver=sgd; total time= 3.0min
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:528: FitFailedWarning:
-21 fits failed out of a total of 144.
-The score on these train-test partitions for these parameters will be set to nan.
-If these failures are not expected, you can try to debug them by setting error_score='raise'.
-
-Below are more details about the failures:
---------------------------------------------------------------------------------
-21 fits failed with the following error:
-Traceback (most recent call last):
- File "/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/model_selection/_validation.py", line 866, in _fit_and_score
- estimator.fit(X_train, y_train, **fit_params)
- File "/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/base.py", line 1389, in wrapper
- return fit_method(estimator, *args, **kwargs)
- File "/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py", line 754, in fit
- return self._fit(X, y, incremental=False)
- File "/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py", line 496, in _fit
- raise ValueError(
-ValueError: Solver produced non-finite parameter weights. The input data may contain large values and need to be preprocessed.
-
- warnings.warn(some_fits_failed_message, FitFailedWarning)
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/model_selection/_search.py:1108: UserWarning: One or more of the test scores are non-finite: [ 0.54900442 0.55026229 0.55175494 0.54449637 0.54619006 nan
- 0.55486645 0.55397549 0.5576357 nan 0.54283811 nan
- 0.5408644 0.50266413 0.49902525 0.17836774 0.54044313 -0.00213426
- 0.51490498 0.44610184 0.47578625 nan 0.53347605 nan
- 0.55993497 0.5530103 0.55283927 0.5452355 0.52950521 0.20815397
- 0.55149549 0.54391725 0.5429399 0.53528469 0.53307444 nan
- 0.49646465 0.48566926 0.51478239 0.54206741 0.49009549 nan
- 0.48790796 0.40424641 0.4902751 0.53533079 0.48903065 nan]
- warnings.warn(
-/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
- warnings.warn(
-Meilleurs paramètres : {'activation': 'tanh', 'hidden_layer_sizes': (50,), 'learning_rate_init': 0.001, 'max_iter': 500, 'solver': 'adam'}
-📊 MLPRegressor Optimisé - MAE: 9.16, RMSE: 12.46, R²: 0.578
- id ... pred_mlp
-0 7pQSmQ0l7QdBeL9X6CEMbH ... NaN
-1 2LcJoQ5SDUZrC2qUjWMEdF ... NaN
-2 6RzHyUtRNARYyn2AIuoLnY ... NaN
-3 6Kd0I5es8911FZpYhFS053 ... NaN
-4 0IhY390qx5QJEnRXpeuEwq ... NaN
-5 1e7M98usgS7tK89PoEbqpz ... NaN
-6 7mZwaEEaHSeTYC2hTAjUki ... NaN
-7 0uzPDHGV0ZAjk8wLlryt7C ... NaN
-8 45t91j2BnZBTBtechHEdEo ... 38.985588
-9 0CIRYGtFan5C3t1udR9A2p ... NaN
-10 4qgBYQNjvnuwxp9a68LG0t ... NaN
-11 3PHtZBkUc9dDxnaVH9lpTd ... NaN
-12 0PFRTA04YOjj4VT3O6MpgP ... NaN
-13 1uRKT2LRANv4baowBWHfDS ... NaN
-14 6OgG8LnH5gjxlOmBrogYo5 ... NaN
-15 3UT1XINeKNAmRyaCHWBOVt ... NaN
-16 1ckIrPqEU43EltNMg8HzlV ... NaN
-17 4ZHPfdvTgCDGIeP8fAUVpG ... NaN
-18 5U1cEuyrUubzj5gc5T7uND ... NaN
-19 0RbSPGMjoqjO0GjPFozPkY ... NaN
-20 0Cwl389eg6mvLxcaAbV24Z ... NaN
-21 0W18O5YKX34dnMqN8MQFfK ... NaN
-22 5hNX9VwbZXJ29NW79dDeDz ... NaN
-23 3RDMd9JiKdVik8zjGhZ0wJ ... NaN
-24 7MER2sjr6GgSohO2vpNO2g ... NaN
-25 7GDciQOihRs8nQFEFOrc5K ... NaN
-26 24ejNAm1vGCiaB9GBLos4n ... NaN
-27 1oXJHuq86np471uQGQjfqg ... NaN
-28 3jfdw2ubsW6gUXrf8MAggA ... NaN
-29 0awP7BRdUvnzBCaU9YOpiJ ... NaN
-30 3vsv5Rj6zpoIvzl8ciwIpd ... NaN
-31 15CxpYrIBCl3PX2sVdgDzw ... NaN
-32 6vAyOOwgDTH5LCLY2glRTl ... NaN
-33 7JZUD0GPmCKtY5XJWL0dJl ... NaN
-34 3Wqq9SNrWfdQWqQg4xeYLo ... NaN
-35 3ZgQhe1Sv7mnQjpVTJnOFp ... NaN
-36 6IEjcFyfMHwQbe26jLojUj ... NaN
-37 5jkjpSsMOfsxgdGScPZVq2 ... NaN
-38 42alprYTLPWivGUUbuR4dD ... NaN
-39 7C8FT11joTPgBCsoDqG735 ... 30.940608
-
-[40 rows x 28 columns]