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Refactor code for improved readability and consistency across notebooks

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- Standardized spacing around operators and function arguments in TP7_Kmeans.ipynb and neural_network.ipynb.
- Enhanced the formatting of model building and training code in neural_network.ipynb for better clarity.
- Updated the pyproject.toml to remove a specific TensorFlow version and added linting configuration for Ruff.
- Improved comments and organization in the code to facilitate easier understanding and maintenance.
This commit is contained in:
Arthur Danjou
2025-07-01 20:46:08 +02:00
parent e273cf90f7
commit f94ff07cab
34 changed files with 5713 additions and 5047 deletions
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516
M1/Statistical Learning/TP0_Intro_Jupyter_Python.ipynb
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M1/Statistical Learning/TP1_A_first_example.ipynb
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M1/Statistical Learning/TP2_KNN.ipynb
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M1/Statistical Learning/TP2_bis_OPTIONAL.ipynb
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M1/Statistical Learning/TP3_Logistic_Regression_and _SGD.ipynb
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M1/Statistical Learning/TP4_Ridge_Lasso_and_CV.ipynb
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@@ -43,7 +43,7 @@
"source": [
"import warnings\n",
"\n",
"warnings.filterwarnings('ignore')"
"warnings.filterwarnings(\"ignore\")"
]
},
{
@@ -434,7 +434,7 @@
],
"source": [
"import numpy as np\n",
"import pandas as pd # dataframes are in pandas \n",
"import pandas as pd # dataframes are in pandas\n",
"import matplotlib.pyplot as plt\n",
"\n",
"hitters = pd.read_csv(\"data/Hitters.csv\", index_col=\"Name\")\n",
@@ -895,9 +895,13 @@
],
"source": [
"# Hint for Question (4) :\n",
"ex = pd.DataFrame(dict(nom=['Alice', 'Nicolas', 'Jean'],\n",
" age=[19, np.NaN, np.NaN],\n",
" exam=[15, 14, np.NaN]))\n",
"ex = pd.DataFrame(\n",
" dict(\n",
" nom=[\"Alice\", \"Nicolas\", \"Jean\"],\n",
" age=[19, np.NaN, np.NaN],\n",
" exam=[15, 14, np.NaN],\n",
" )\n",
")\n",
"\n",
"print(\"data : \\n\", ex)\n",
"print(\"First result : \\n\", ex.isnull())\n",
@@ -1080,10 +1084,10 @@
],
"source": [
"# We remove the players for whom Salary is missing\n",
"hitters.dropna(subset=['Salary'], inplace=True)\n",
"hitters.dropna(subset=[\"Salary\"], inplace=True)\n",
"\n",
"X = hitters.select_dtypes(include=int)\n",
"Y = hitters['Salary']\n",
"Y = hitters[\"Salary\"]\n",
"\n",
"# check-point\n",
"print(Y.isnull().sum()) # should be 0\n",
@@ -1109,7 +1113,7 @@
},
"outputs": [],
"source": [
"#Answer for Exercise 4\n",
"# Answer for Exercise 4\n",
"from sklearn.model_selection import train_test_split\n",
"\n",
"Xtrain, Xtest, Ytrain, Ytest = train_test_split(X, Y, test_size=0.3, random_state=42)"
@@ -1697,8 +1701,8 @@
}
],
"source": [
"#the values of alphas chosen by defaults are also on a logarithmic scale\n",
"plt.plot(np.log10(alphas_lasso), '.')"
"# the values of alphas chosen by defaults are also on a logarithmic scale\n",
"plt.plot(np.log10(alphas_lasso), \".\")"
]
},
{
@@ -1735,8 +1739,8 @@
"source": [
"fig, ax = plt.subplots(figsize=(8, 6))\n",
"ax.plot(np.log10(alphas_lasso), coefs_lasso)\n",
"ax.set_xlabel('log10(alpha)')\n",
"ax.set_ylabel('Lasso coefficients')"
"ax.set_xlabel(\"log10(alpha)\")\n",
"ax.set_ylabel(\"Lasso coefficients\")"
]
},
{
@@ -1792,7 +1796,7 @@
"print(\"1.\\n\", ind)\n",
"print(\"2.\\n\", ind == 0)\n",
"print(\"3. Le nombre de 0 de chaque colonne est :\\n \", (ind == 0).sum(axis=0))\n",
"print(\"4. Le nombre de 0 de chaque ligne est : \\n\", (ind == 0).sum(axis=1))\n"
"print(\"4. Le nombre de 0 de chaque ligne est : \\n\", (ind == 0).sum(axis=1))"
]
},
{
@@ -2294,18 +2298,19 @@
"from sklearn.linear_model import LinearRegression\n",
"\n",
"linReg = LinearRegression()\n",
"linReg.fit(Xtrain,\n",
" Ytrain) # no need to scale for OLS if you just want to predict (unless the solver works best with scaled data)\n",
"linReg.fit(\n",
" Xtrain, Ytrain\n",
") # no need to scale for OLS if you just want to predict (unless the solver works best with scaled data)\n",
"# the predictions should not be different with or without standardization (could differ only owing to numerical problems)\n",
"hatY_LinReg = linReg.predict(Xtest)\n",
"\n",
"fig, ax = plt.subplots()\n",
"ax.scatter(Ytest, hatY_LinReg, s=5)\n",
"ax.plot([0, 1], [0, 1], transform=ax.transAxes, ls='--', c='gray')\n",
"ax.set_xlabel('Ytest')\n",
"ax.set_ylabel('hatY')\n",
"ax.set_title('Predicted vs true salaries for OLS estimator')\n",
"ax.axis('square')"
"ax.plot([0, 1], [0, 1], transform=ax.transAxes, ls=\"--\", c=\"gray\")\n",
"ax.set_xlabel(\"Ytest\")\n",
"ax.set_ylabel(\"hatY\")\n",
"ax.set_title(\"Predicted vs true salaries for OLS estimator\")\n",
"ax.axis(\"square\")"
]
},
{
@@ -2355,11 +2360,11 @@
"\n",
"fig, ax = plt.subplots()\n",
"ax.scatter(Ytest, hatY_ridge, s=5)\n",
"ax.plot([0, 1], [0, 1], transform=ax.transAxes, ls='--', c='gray')\n",
"ax.set_xlabel('Ytest')\n",
"ax.set_ylabel('hatY')\n",
"ax.set_title('Predicted vs true salaries for Ridge estimator')\n",
"ax.axis('square')"
"ax.plot([0, 1], [0, 1], transform=ax.transAxes, ls=\"--\", c=\"gray\")\n",
"ax.set_xlabel(\"Ytest\")\n",
"ax.set_ylabel(\"hatY\")\n",
"ax.set_title(\"Predicted vs true salaries for Ridge estimator\")\n",
"ax.axis(\"square\")"
]
},
{
@@ -2408,11 +2413,11 @@
"\n",
"fig, ax = plt.subplots()\n",
"ax.scatter(Ytest, hatY_lasso, s=5)\n",
"ax.plot([0, 1], [0, 1], transform=ax.transAxes, ls='--', c='gray')\n",
"ax.set_xlabel('Ytest')\n",
"ax.set_ylabel('hatY')\n",
"ax.set_title('Predicted vs true salaries for Ridge estimator')\n",
"ax.axis('square')"
"ax.plot([0, 1], [0, 1], transform=ax.transAxes, ls=\"--\", c=\"gray\")\n",
"ax.set_xlabel(\"Ytest\")\n",
"ax.set_ylabel(\"hatY\")\n",
"ax.set_title(\"Predicted vs true salaries for Ridge estimator\")\n",
"ax.axis(\"square\")"
]
},
{
@@ -2445,7 +2450,7 @@
"source": [
"from sklearn.linear_model import LassoLarsIC\n",
"\n",
"lassoBIC = LassoLarsIC(criterion='bic')\n",
"lassoBIC = LassoLarsIC(criterion=\"bic\")\n",
"lassoBIC.fit(XtrainScaled, Ytrain)\n",
"print(\"best alpha chosen by BIC criterion :\", lassoBIC.alpha_)\n",
"print(\"best alpha chosen by CV :\", lassoCV.alpha_)\n",
@@ -2499,11 +2504,11 @@
"\n",
"fig, ax = plt.subplots()\n",
"ax.scatter(Ytest, hatY_BIC, s=5)\n",
"ax.plot([0, 1], [0, 1], transform=ax.transAxes, ls='--', c='gray')\n",
"ax.set_xlabel('Ytest')\n",
"ax.set_ylabel('hatY')\n",
"ax.set_title('Predicted vs true salaries for LassoBIC estimator')\n",
"ax.axis('square')"
"ax.plot([0, 1], [0, 1], transform=ax.transAxes, ls=\"--\", c=\"gray\")\n",
"ax.set_xlabel(\"Ytest\")\n",
"ax.set_ylabel(\"hatY\")\n",
"ax.set_title(\"Predicted vs true salaries for LassoBIC estimator\")\n",
"ax.axis(\"square\")"
]
},
{
@@ -2539,7 +2544,9 @@
"from sklearn.metrics import mean_squared_error\n",
"\n",
"MSEs = []\n",
"for name, estimator in zip([\"LassoCV\", \"LassoBIC\", \"RidgeCV\", \"OLS\"], [lassoCV, lassoBIC, ridgeCV, linReg]):\n",
"for name, estimator in zip(\n",
" [\"LassoCV\", \"LassoBIC\", \"RidgeCV\", \"OLS\"], [lassoCV, lassoBIC, ridgeCV, linReg]\n",
"):\n",
" y_pred = estimator.predict(Xtest)\n",
" MSE = mean_squared_error(Ytest, y_pred)\n",
" print(f\"MSE for {name} : {MSE}\")\n",
@@ -2584,10 +2591,12 @@
"ols_errors = np.abs(Ytest - linReg.predict(Xtest))\n",
"\n",
"fig, ax = plt.subplots(figsize=(10, 6))\n",
"ax.boxplot([ridge_cv_errors, lasso_cv_errors, lasso_bic_errors, ols_errors],\n",
" labels=['RidgeCV', 'LassoCV', 'LassoBIC', 'OLS'])\n",
"ax.set_title('Boxplot of Absolute Errors')\n",
"ax.set_ylabel('Absolute Error')\n",
"ax.boxplot(\n",
" [ridge_cv_errors, lasso_cv_errors, lasso_bic_errors, ols_errors],\n",
" labels=[\"RidgeCV\", \"LassoCV\", \"LassoBIC\", \"OLS\"],\n",
")\n",
"ax.set_title(\"Boxplot of Absolute Errors\")\n",
"ax.set_ylabel(\"Absolute Error\")\n",
"plt.show()"
]
},

128
M1/Statistical Learning/TP5_Naive_Bayes.ipynb
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@@ -32,7 +32,7 @@
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd \n",
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt"
]
@@ -226,7 +226,7 @@
}
],
"source": [
"sms = pd.read_csv(\"data/spam.csv\", encoding='latin')\n",
"sms = pd.read_csv(\"data/spam.csv\", encoding=\"latin\")\n",
"\n",
"sms.head()"
]
@@ -244,7 +244,7 @@
"metadata": {},
"outputs": [],
"source": [
"sms.rename(columns={'v1':'Label', 'v2':'Text'}, inplace=True)"
"sms.rename(columns={\"v1\": \"Label\", \"v2\": \"Text\"}, inplace=True)"
]
},
{
@@ -644,7 +644,7 @@
}
],
"source": [
"sms['Labelnum']=sms['Label'].map({'ham':0,'spam':1})\n",
"sms[\"Labelnum\"] = sms[\"Label\"].map({\"ham\": 0, \"spam\": 1})\n",
"\n",
"sms.head()"
]
@@ -674,13 +674,13 @@
}
],
"source": [
"# Hint 1 for Exercise 1 \n",
"a=np.array([0,1,1,1,0])\n",
"print (len(a))\n",
"print (a[a==0])\n",
"print (len(a[a==0]))\n",
"print (a[a==1])\n",
"print (len(a[a==1]))"
"# Hint 1 for Exercise 1\n",
"a = np.array([0, 1, 1, 1, 0])\n",
"print(len(a))\n",
"print(a[a == 0])\n",
"print(len(a[a == 0]))\n",
"print(a[a == 1])\n",
"print(len(a[a == 1]))"
]
},
{
@@ -881,8 +881,8 @@
}
],
"source": [
"# Hint 2 for Exercise 1 \n",
"sms[sms.Labelnum==0].head()"
"# Hint 2 for Exercise 1\n",
"sms[sms.Labelnum == 0].head()"
]
},
{
@@ -1083,8 +1083,8 @@
}
],
"source": [
"# Hint 3 for Exercise 1 \n",
"sms[sms.Labelnum==1].head()"
"# Hint 3 for Exercise 1\n",
"sms[sms.Labelnum == 1].head()"
]
},
{
@@ -1104,8 +1104,8 @@
],
"source": [
"print(len(sms))\n",
"print(sms[sms.Label == 'ham'].shape)\n",
"print(sms[sms.Label == 'spam'].shape)"
"print(sms[sms.Label == \"ham\"].shape)\n",
"print(sms[sms.Label == \"spam\"].shape)"
]
},
{
@@ -1136,8 +1136,8 @@
],
"source": [
"# Hint 1 for Exercise 2\n",
"print (sms.loc[0, 'Text']) \n",
"print (\"--> The length of the first sms is\", len(sms.loc[0, 'Text']))"
"print(sms.loc[0, \"Text\"])\n",
"print(\"--> The length of the first sms is\", len(sms.loc[0, \"Text\"]))"
]
},
{
@@ -1160,10 +1160,13 @@
],
"source": [
"plt.figure(figsize=(10, 6))\n",
"plt.hist(sms.loc[:, 'Text'].apply(len), bins='stone',)\n",
"plt.title('Histogram of SMS Lengths')\n",
"plt.xlabel('Length')\t\n",
"plt.ylabel('Frequency')\n",
"plt.hist(\n",
" sms.loc[:, \"Text\"].apply(len),\n",
" bins=\"stone\",\n",
")\n",
"plt.title(\"Histogram of SMS Lengths\")\n",
"plt.xlabel(\"Length\")\n",
"plt.ylabel(\"Frequency\")\n",
"plt.show()"
]
},
@@ -1222,30 +1225,41 @@
}
],
"source": [
"Example = pd.DataFrame([['iphone gratuit iphone gratuit',1],['mille vert gratuit',0],\n",
" ['iphone mille euro',0],['argent gratuit euro gratuit',1]],\n",
" columns=['sms', 'label'])\n",
"Example = pd.DataFrame(\n",
" [\n",
" [\"iphone gratuit iphone gratuit\", 1],\n",
" [\"mille vert gratuit\", 0],\n",
" [\"iphone mille euro\", 0],\n",
" [\"argent gratuit euro gratuit\", 1],\n",
" ],\n",
" columns=[\"sms\", \"label\"],\n",
")\n",
"vec = CountVectorizer()\n",
"X = vec.fit_transform(Example.sms)\n",
"\n",
"# 1. Displaying the vocabulary\n",
"\n",
"print (\"1. The vocabulary of Example is \", vec.vocabulary_)\n",
"print(\"1. The vocabulary of Example is \", vec.vocabulary_)\n",
"\n",
"# 1 bis :\n",
"\n",
"print('The vocabulary arranged in alphabetical order : ', sorted(list(vec.vocabulary_.keys())))\n",
"print(\n",
" \"The vocabulary arranged in alphabetical order : \",\n",
" sorted(list(vec.vocabulary_.keys())),\n",
")\n",
"\n",
"# 2. Displaying the vectors : \n",
"# 2. Displaying the vectors :\n",
"\n",
"print (\"2. The vectors corresponding to the sms are : \\n\", X.toarray())# X.toarray because \n",
"# X is a \"sparse\" matrix. \n",
"print(\n",
" \"2. The vectors corresponding to the sms are : \\n\", X.toarray()\n",
") # X.toarray because\n",
"# X is a \"sparse\" matrix.\n",
"\n",
"# 3. For a new data x_0=\"iphone gratuit\", \n",
"# you must also transform x_0 into a numerical vector before predicting. \n",
"# 3. For a new data x_0=\"iphone gratuit\",\n",
"# you must also transform x_0 into a numerical vector before predicting.\n",
"\n",
"vec_x_0=vec.transform(['iphone gratuit']).toarray() # \n",
"print (\"3. The numerical vector corresponding to (x_0=iphone gratuit) is \\n\", vec_x_0 )"
"vec_x_0 = vec.transform([\"iphone gratuit\"]).toarray() #\n",
"print(\"3. The numerical vector corresponding to (x_0=iphone gratuit) is \\n\", vec_x_0)"
]
},
{
@@ -1267,7 +1281,7 @@
],
"source": [
"#'sparse' version (without \"to_array\")\n",
"v = vec.transform(['iphone iphone gratuit'])\n",
"v = vec.transform([\"iphone iphone gratuit\"])\n",
"v"
]
},
@@ -1309,8 +1323,8 @@
}
],
"source": [
"# \"(0,2) 1\" means : the element in row 0 and column 2 is equal to 1. \n",
"# \"(0,3) 2\" means : the element in row 0 and column 3 is equal to 2. \n",
"# \"(0,2) 1\" means : the element in row 0 and column 2 is equal to 1.\n",
"# \"(0,3) 2\" means : the element in row 0 and column 3 is equal to 2.\n",
"print(v)"
]
},
@@ -1340,8 +1354,8 @@
}
],
"source": [
"vec_x_1 = vec.transform(['iphone vert gratuit']).toarray()\n",
"vec_x_2 = vec.transform(['iphone rouge gratuit']).toarray()\n",
"vec_x_1 = vec.transform([\"iphone vert gratuit\"]).toarray()\n",
"vec_x_2 = vec.transform([\"iphone rouge gratuit\"]).toarray()\n",
"print(vec_x_1)\n",
"print(vec_x_2)"
]
@@ -1372,8 +1386,8 @@
"outputs": [],
"source": [
"vectorizer = CountVectorizer()\n",
"X = vectorizer.fit_transform(sms['Text'])\n",
"y = sms['Labelnum']"
"X = vectorizer.fit_transform(sms[\"Text\"])\n",
"y = sms[\"Labelnum\"]"
]
},
{
@@ -1400,10 +1414,12 @@
"source": [
"from sklearn.model_selection import train_test_split\n",
"\n",
"X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.30,random_state=50)\n",
"X_train, X_test, y_train, y_test = train_test_split(\n",
" X, y, test_size=0.30, random_state=50\n",
")\n",
"\n",
"print (\"size of the training set: \", X_train.shape[0])\n",
"print (\"size of the test set :\", X_test.shape[0])"
"print(\"size of the training set: \", X_train.shape[0])\n",
"print(\"size of the test set :\", X_test.shape[0])"
]
},
{
@@ -1906,7 +1922,7 @@
"from sklearn.metrics import accuracy_score\n",
"\n",
"y_pred = sms_bayes.predict(X_test)\n",
"print (\"The accuracy score on the test set is \", accuracy_score(y_test, y_pred))"
"print(\"The accuracy score on the test set is \", accuracy_score(y_test, y_pred))"
]
},
{
@@ -1969,10 +1985,17 @@
}
],
"source": [
"my_sms = vectorizer.transform(['free trial!', 'Iphone 15 is now free', 'I want coffee', 'I want to buy a new iphone'])\n",
"my_sms = vectorizer.transform(\n",
" [\n",
" \"free trial!\",\n",
" \"Iphone 15 is now free\",\n",
" \"I want coffee\",\n",
" \"I want to buy a new iphone\",\n",
" ]\n",
")\n",
"\n",
"pred_my_sms = sms_bayes.predict(my_sms)\n",
"print (pred_my_sms)"
"print(pred_my_sms)"
]
},
{
@@ -1999,7 +2022,7 @@
"from sklearn.naive_bayes import BernoulliNB\n",
"\n",
"# Load the MNIST dataset\n",
"mnist = fetch_openml('mnist_784', version=1, parser='auto')\n",
"mnist = fetch_openml(\"mnist_784\", version=1, parser=\"auto\")\n",
"X, y = mnist.data, mnist.target"
]
},
@@ -2036,7 +2059,9 @@
"source": [
"X_copy = (X.copy() >= 127).astype(int)\n",
"\n",
"X_train, X_test, y_train, y_test = train_test_split(X_copy, y, test_size=0.25, random_state=42)\n",
"X_train, X_test, y_train, y_test = train_test_split(\n",
" X_copy, y, test_size=0.25, random_state=42\n",
")\n",
"\n",
"ber_bayes = BernoulliNB()\n",
"ber_bayes.fit(X_train, y_train)\n",
@@ -2059,6 +2084,7 @@
"outputs": [],
"source": [
"from keras.datasets import cifar10\n",
"\n",
"(x_train, y_train), (x_test, y_test) = cifar10.load_data()"
]
},
@@ -2077,7 +2103,7 @@
}
],
"source": [
"# reminder : the output is an RGB image 32 x 32 \n",
"# reminder : the output is an RGB image 32 x 32\n",
"print(x_train.shape)\n",
"print(y_train.shape)"
]

4467
M1/Statistical Learning/TP6_keras_intro.ipynb
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M1/Statistical Learning/TP7_Kmeans.ipynb
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@@ -46,23 +46,23 @@
"\n",
"\n",
"np.random.seed(12)\n",
"num_observations=400\n",
"num_observations = 400\n",
"\n",
"center1=[0,0]\n",
"center2=[1,4]\n",
"center3=[-3,2]\n",
"center1 = [0, 0]\n",
"center2 = [1, 4]\n",
"center3 = [-3, 2]\n",
"\n",
"x1=np.random.multivariate_normal(center1,[[1,0],[0,1]], num_observations)\n",
"x2=np.random.multivariate_normal(center2,[[1,0],[0,1]], num_observations)\n",
"x3=np.random.multivariate_normal(center3,[[1,0],[0,1]], num_observations)\n",
"x1 = np.random.multivariate_normal(center1, [[1, 0], [0, 1]], num_observations)\n",
"x2 = np.random.multivariate_normal(center2, [[1, 0], [0, 1]], num_observations)\n",
"x3 = np.random.multivariate_normal(center3, [[1, 0], [0, 1]], num_observations)\n",
"\n",
"X= np.vstack((x1, x2, x3)).astype(np.float32)\n",
"X = np.vstack((x1, x2, x3)).astype(np.float32)\n",
"\n",
"plt.figure(figsize=(8,6))\n",
"plt.plot(X[:,0], X[:,1],\".b\",alpha=0.2)\n",
"plt.plot(center1[0], center1[1], '.', color='red', markersize=10)\n",
"plt.plot(center2[0], center2[1], '.', color='red', markersize=10)\n",
"plt.plot(center3[0], center3[1], '.', color='red', markersize=10)\n",
"plt.figure(figsize=(8, 6))\n",
"plt.plot(X[:, 0], X[:, 1], \".b\", alpha=0.2)\n",
"plt.plot(center1[0], center1[1], \".\", color=\"red\", markersize=10)\n",
"plt.plot(center2[0], center2[1], \".\", color=\"red\", markersize=10)\n",
"plt.plot(center3[0], center3[1], \".\", color=\"red\", markersize=10)\n",
"plt.show()"
]
},
@@ -540,10 +540,12 @@
}
],
"source": [
"plt.figure(figsize=(8,6))\n",
"plt.plot(X[:,0], X[:,1],\".b\",alpha=0.2)\n",
"plt.figure(figsize=(8, 6))\n",
"plt.plot(X[:, 0], X[:, 1], \".b\", alpha=0.2)\n",
"for center in kmeans1.cluster_centers_:\n",
" plt.plot(center[0], center[1], '.', color='red', markersize=10, label='Cluster center')\n",
" plt.plot(\n",
" center[0], center[1], \".\", color=\"red\", markersize=10, label=\"Cluster center\"\n",
" )\n",
"plt.legend()\n",
"plt.show()"
]
@@ -585,11 +587,11 @@
"# Hint: An example for plotting the Voronoi partition\n",
"from scipy.spatial import Voronoi, voronoi_plot_2d\n",
"\n",
"points_generer_voronoi = np.array([[0,0],[1,4],[-3,2]])\n",
"points_generer_voronoi = np.array([[0, 0], [1, 4], [-3, 2]])\n",
"\n",
"vor = Voronoi(points_generer_voronoi)\n",
"\n",
"fig, ax = plt.subplots(1,1,figsize=(4,4)) \n",
"fig, ax = plt.subplots(1, 1, figsize=(4, 4))\n",
"\n",
"fig = voronoi_plot_2d(vor, ax=ax, show_vertices=False)"
]
@@ -614,14 +616,16 @@
"# Answer for Exercise 3\n",
"\n",
"\n",
"fig, ax = plt.subplots(1,1,figsize=(8,6)) \n",
"plt.plot(X[:,0], X[:,1], \".b\", alpha=0.2)\n",
"fig, ax = plt.subplots(1, 1, figsize=(8, 6))\n",
"plt.plot(X[:, 0], X[:, 1], \".b\", alpha=0.2)\n",
"\n",
"vor = Voronoi(kmeans1.cluster_centers_)\n",
"fig = voronoi_plot_2d(vor, ax=ax, show_vertices=False)\n",
"\n",
"for center in kmeans1.cluster_centers_:\n",
" plt.plot(center[0], center[1], '.', color='red', markersize=10, label='Cluster center')\n",
" plt.plot(\n",
" center[0], center[1], \".\", color=\"red\", markersize=10, label=\"Cluster center\"\n",
" )\n",
"plt.legend()\n",
"plt.show()"
]
@@ -1233,10 +1237,10 @@
}
],
"source": [
"print (\"1:\", compress_model.labels_)\n",
"print (\"2:\", compress_model.labels_.shape)\n",
"print (\"3:\", compress_model.cluster_centers_)\n",
"print (\"4:\", compress_model.cluster_centers_.shape)"
"print(\"1:\", compress_model.labels_)\n",
"print(\"2:\", compress_model.labels_.shape)\n",
"print(\"3:\", compress_model.cluster_centers_)\n",
"print(\"4:\", compress_model.cluster_centers_.shape)"
]
},
{
@@ -1275,13 +1279,13 @@
"metadata": {},
"outputs": [],
"source": [
"color_new=np.zeros_like(colors)\n",
"color_new = np.zeros_like(colors)\n",
"\n",
"labels=compress_model.labels_\n",
"centers=compress_model.cluster_centers_\n",
"labels = compress_model.labels_\n",
"centers = compress_model.cluster_centers_\n",
"\n",
"for i in range(len(colors)):\n",
" color_new[i]= centers[labels[i]]"
" color_new[i] = centers[labels[i]]"
]
},
{
@@ -1336,11 +1340,12 @@
],
"source": [
"import matplotlib.image as mpimg\n",
"\n",
"mpimg.imsave(\"assets/zelda_new.png\", zelda_new)\n",
"\n",
"plt.figure(figsize=(8, 6))\n",
"plt.imshow(zelda_new)\n",
"plt.show()\n"
"plt.show()"
]
},
{
@@ -1363,13 +1368,13 @@
"source": [
"import os\n",
"\n",
"size_new=os.path.getsize('assets/zelda_new.png')\n",
"size_old=os.path.getsize('assets/zelda.png')\n",
"size_new = os.path.getsize(\"assets/zelda_new.png\")\n",
"size_old = os.path.getsize(\"assets/zelda.png\")\n",
"\n",
"print (\"The original size is \", size_old, \"bytes.\")\n",
"print (\"The compressed size is \", size_new, \"bytes.\")\n",
"print(\"The original size is \", size_old, \"bytes.\")\n",
"print(\"The compressed size is \", size_new, \"bytes.\")\n",
"\n",
"print (f\"The compression factor is {size_old/size_new : .3f}\")"
"print(f\"The compression factor is {size_old / size_new: .3f}\")"
]
},
{
@@ -1407,8 +1412,8 @@
}
],
"source": [
"partiel=plt.imread(\"assets/partiel.png\")\n",
"plt.figure(figsize = (20,10))\n",
"partiel = plt.imread(\"assets/partiel.png\")\n",
"plt.figure(figsize=(20, 10))\n",
"plt.imshow(partiel)"
]
},
@@ -1426,7 +1431,7 @@
}
],
"source": [
"print (partiel.shape)"
"print(partiel.shape)"
]
},
{
@@ -1472,23 +1477,23 @@
}
],
"source": [
"partiel_new=np.zeros_like(partiel)\n",
"partiel_new = np.zeros_like(partiel)\n",
"\n",
"noir_rgb=np.array([0,0,0])\n",
"blanc_rgb=np.array([1,1,1])\n",
"noir_rgb = np.array([0, 0, 0])\n",
"blanc_rgb = np.array([1, 1, 1])\n",
"\n",
"epsilon = 0.5 # threshold\n",
"\n",
"epsilon=0.5 # threshold\n",
" \n",
"distances = np.linalg.norm(partiel - noir_rgb, axis=2)\n",
"partiel_new = np.zeros_like(partiel)\n",
"partiel_new[distances <= epsilon] = noir_rgb\n",
"partiel_new[distances > epsilon] = blanc_rgb\n",
" \n",
"\n",
"mpimg.imsave(\"assets/partiel_new.png\", partiel_new)\n",
"\n",
"plt.figure(figsize=(20,10))\n",
"plt.figure(figsize=(20, 10))\n",
"plt.imshow(partiel_new)\n",
"plt.show()\n"
"plt.show()"
]
},
{
@@ -1531,16 +1536,20 @@
"mnist = tf.keras.datasets.mnist\n",
"(X_train, y_train), (X_test, y_test) = mnist.load_data()\n",
"\n",
"X_train = X_train.reshape(-1, 28*28)\n",
"X_train = X_train.reshape(-1, 28 * 28)\n",
"\n",
"kmeans2 = KMeans(n_clusters=10)\n",
"clusters = kmeans2.fit_predict(X_train)\n",
"\n",
"\n",
"def map_clusters_to_labels(clusters, true_labels):\n",
" return np.array([mode(true_labels[clusters == i], keepdims=True).mode[0] for i in range(10)])\n",
" return np.array(\n",
" [mode(true_labels[clusters == i], keepdims=True).mode[0] for i in range(10)]\n",
" )\n",
"\n",
"\n",
"cluster_to_label = map_clusters_to_labels(clusters, y_train)\n",
"print(\"Cluster to label mapping:\", cluster_to_label)\n"
"print(\"Cluster to label mapping:\", cluster_to_label)"
]
},
{

83
M1/Statistical Learning/neural_network.ipynb
View File

@@ -6,7 +6,7 @@
"metadata": {},
"outputs": [],
"source": [
"import numpy as np \n",
"import numpy as np\n",
"import pandas as pd\n",
"import tensorflow as tf\n",
"import matplotlib.pyplot as plt"
@@ -178,16 +178,21 @@
"outputs": [],
"source": [
"def build_model():\n",
" model = tf.keras.models.Sequential([\n",
" tf.keras.layers.Dense(16, activation='relu', input_shape=(X.shape[1],), kernel_regularizer=tf.keras.regularizers.l2(0.01)),\n",
" tf.keras.layers.Dense(8, activation='relu', kernel_regularizer=tf.keras.regularizers.l2(0.01)),\n",
" tf.keras.layers.Dense(1, activation='sigmoid')\n",
" ])\n",
" model.compile(\n",
" optimizer='adam',\n",
" loss='binary_crossentropy',\n",
" metrics=['accuracy']\n",
" model = tf.keras.models.Sequential(\n",
" [\n",
" tf.keras.layers.Dense(\n",
" 16,\n",
" activation=\"relu\",\n",
" input_shape=(X.shape[1],),\n",
" kernel_regularizer=tf.keras.regularizers.l2(0.01),\n",
" ),\n",
" tf.keras.layers.Dense(\n",
" 8, activation=\"relu\", kernel_regularizer=tf.keras.regularizers.l2(0.01)\n",
" ),\n",
" tf.keras.layers.Dense(1, activation=\"sigmoid\"),\n",
" ]\n",
" )\n",
" model.compile(optimizer=\"adam\", loss=\"binary_crossentropy\", metrics=[\"accuracy\"])\n",
" return model"
]
},
@@ -291,10 +296,7 @@
"histories = []\n",
"\n",
"early_stopping = EarlyStopping(\n",
" monitor='val_loss',\n",
" patience=10,\n",
" restore_best_weights=True,\n",
" verbose=1\n",
" monitor=\"val_loss\", patience=10, restore_best_weights=True, verbose=1\n",
")\n",
"\n",
"for fold, (train_idx, val_idx) in enumerate(skf.split(X, y), 1):\n",
@@ -305,29 +307,28 @@
" scaler = StandardScaler()\n",
" X_train_scaled = scaler.fit_transform(X_train)\n",
" X_val_scaled = scaler.transform(X_val)\n",
" \n",
"\n",
" model = build_model()\n",
"\n",
" model.compile(\n",
" optimizer='adam',\n",
" loss='binary_crossentropy',\n",
" metrics=[\"f1_score\"]\n",
" )\n",
" model.compile(optimizer=\"adam\", loss=\"binary_crossentropy\", metrics=[\"f1_score\"])\n",
"\n",
" # EarlyStopping\n",
" callback = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=10, restore_best_weights=True)\n",
" callback = tf.keras.callbacks.EarlyStopping(\n",
" monitor=\"val_loss\", patience=10, restore_best_weights=True\n",
" )\n",
"\n",
" # Entraînement\n",
" history = model.fit(\n",
" X_train_scaled, y_train,\n",
" X_train_scaled,\n",
" y_train,\n",
" epochs=50,\n",
" batch_size=8,\n",
" validation_data=(X_val_scaled, y_val),\n",
" callbacks=[callback],\n",
" verbose=0,\n",
" class_weight={0: 1.0, 1: 2.0}\n",
" class_weight={0: 1.0, 1: 2.0},\n",
" )\n",
" \n",
"\n",
" histories.append(history.history)\n",
"\n",
" # Prédiction & F1\n",
@@ -360,9 +361,9 @@
"axes = axes.flatten() # Flatten to easily iterate\n",
"\n",
"for i, (hist, ax) in enumerate(zip(histories, axes)):\n",
" ax.plot(hist['loss'], label='Train loss', alpha=0.6)\n",
" ax.plot(hist['val_loss'], label='Val loss', linestyle='--', alpha=0.6)\n",
" ax.set_title(f\"Fold {i+1}\")\n",
" ax.plot(hist[\"loss\"], label=\"Train loss\", alpha=0.6)\n",
" ax.plot(hist[\"val_loss\"], label=\"Val loss\", linestyle=\"--\", alpha=0.6)\n",
" ax.set_title(f\"Fold {i + 1}\")\n",
" ax.set_xlabel(\"Epochs\")\n",
" if i % 2 == 0:\n",
" ax.set_ylabel(\"Binary Crossentropy\")\n",
@@ -436,7 +437,9 @@
"import tensorflow as tf\n",
"import numpy as np\n",
"\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)\n",
"X_train, X_test, y_train, y_test = train_test_split(\n",
" X, y, test_size=0.2, random_state=42, stratify=y\n",
")\n",
"\n",
"scaler = StandardScaler()\n",
"X_train_scaled = scaler.fit_transform(X_train)\n",
@@ -444,21 +447,21 @@
"\n",
"model = build_model()\n",
"\n",
"model.compile(\n",
" optimizer='adam',\n",
" loss='binary_crossentropy'\n",
"model.compile(optimizer=\"adam\", loss=\"binary_crossentropy\")\n",
"\n",
"callback = tf.keras.callbacks.EarlyStopping(\n",
" monitor=\"val_loss\", patience=10, restore_best_weights=True\n",
")\n",
"\n",
"callback = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=10, restore_best_weights=True)\n",
"\n",
"history = model.fit(\n",
" X_train_scaled, y_train,\n",
" X_train_scaled,\n",
" y_train,\n",
" epochs=50,\n",
" batch_size=8,\n",
" validation_split=0.2,\n",
" callbacks=[callback],\n",
" verbose=0,\n",
" class_weight={0: 1.0, 1: 2.0}\n",
" class_weight={0: 1.0, 1: 2.0},\n",
")\n",
"\n",
"\n",
@@ -486,11 +489,11 @@
],
"source": [
"plt.figure(figsize=(8, 5))\n",
"plt.plot(history.history['loss'], label='Loss (train)')\n",
"plt.plot(history.history['val_loss'], label='Loss (val)', linestyle='--')\n",
"plt.xlabel('Epochs')\n",
"plt.ylabel('Binary Cross-Entropy Loss')\n",
"plt.title('Courbe d\\'apprentissage')\n",
"plt.plot(history.history[\"loss\"], label=\"Loss (train)\")\n",
"plt.plot(history.history[\"val_loss\"], label=\"Loss (val)\", linestyle=\"--\")\n",
"plt.xlabel(\"Epochs\")\n",
"plt.ylabel(\"Binary Cross-Entropy Loss\")\n",
"plt.title(\"Courbe d'apprentissage\")\n",
"plt.legend()\n",
"plt.grid(True)\n",
"plt.tight_layout()\n",