correctifs pour lint

.gitignore

@@ -1,2 +1,2 @@
 __pycache__
-.venv
+*/myenv

backend/classification_strategy.py

@@ -0,0 +1,45 @@
+from sklearn.linear_model import LogisticRegression
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import accuracy_score
+from sklearn.preprocessing import LabelEncoder
+
+def perform_classification(data, data_name, target_name, test_size):
+    X = data[data_name]
+    y = data[target_name]
+
+    label_encoders = {}
+    for column in X.select_dtypes(include=['object']).columns:
+        le = LabelEncoder()
+        X[column] = le.fit_transform(X[column])
+        label_encoders[column] = le
+
+    if y.dtype == 'object':
+        le = LabelEncoder()
+        y = le.fit_transform(y)
+        label_encoders[target_name] = le
+    else:
+        if y.nunique() > 10:
+            raise ValueError("The target variable seems to be continuous. Please select a categorical target for classification.")
+    
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size, random_state=42)
+
+    model = LogisticRegression()
+    model.fit(X_train, y_train)
+    y_pred = model.predict(X_test)
+    accuracy = accuracy_score(y_test, y_pred)
+
+    return model, label_encoders, accuracy
+
+def make_prediction(model, label_encoders, data_name, target_name, input_values):
+    X_new = []
+    for feature, value in zip(data_name, input_values):
+        if feature in label_encoders:
+            value = label_encoders[feature].transform([value])[0]
+        X_new.append(value)
+    
+    prediction = model.predict([X_new])
+
+    if target_name in label_encoders:
+        prediction = label_encoders[target_name].inverse_transform(prediction)
+    
+    return prediction[0]

backend/dbscan_strategy.py

@@ -0,0 +1,16 @@
+import matplotlib.pyplot as plt
+from sklearn.cluster import DBSCAN
+
+def perform_dbscan_clustering(data, data_name, eps, min_samples):
+    x = data[data_name].to_numpy()
+    dbscan = DBSCAN(eps=eps, min_samples=min_samples)
+    y_dbscan = dbscan.fit_predict(x)
+
+    fig = plt.figure()
+    if len(data_name) == 2:
+        ax = fig.add_subplot(projection='rectilinear')
+        plt.scatter(x[:, 0], x[:, 1], c=y_dbscan, s=50, cmap="viridis")
+    else:
+        ax = fig.add_subplot(projection='3d')
+        ax.scatter(x[:, 0], x[:, 1], x[:, 2], c=y_dbscan, s=50, cmap="viridis")
+    return fig

backend/kmeans_strategy.py

@@ -0,0 +1,20 @@
+import matplotlib.pyplot as plt
+from sklearn.cluster import KMeans
+
+def perform_kmeans_clustering(data, data_name, n_clusters, n_init, max_iter):
+    x = data[data_name].to_numpy()
+    kmeans = KMeans(n_clusters=n_clusters, init="random", n_init=n_init, max_iter=max_iter, random_state=111)
+    y_kmeans = kmeans.fit_predict(x)
+
+    fig = plt.figure()
+    if len(data_name) == 2:
+        ax = fig.add_subplot(projection='rectilinear')
+        plt.scatter(x[:, 0], x[:, 1], c=y_kmeans, s=50, cmap="viridis")
+        centers = kmeans.cluster_centers_
+        plt.scatter(centers[:, 0], centers[:, 1], c="black", s=200, marker="X")
+    else:
+        ax = fig.add_subplot(projection='3d')
+        ax.scatter(x[:, 0], x[:, 1], x[:, 2], c=y_kmeans, s=50, cmap="viridis")
+        centers = kmeans.cluster_centers_
+        ax.scatter(centers[:, 0], centers[:, 1], centers[:, 2], c="black", s=200, marker="X")
+    return fig

backend/regression_strategy.py

@@ -0,0 +1,18 @@
+from sklearn.linear_model import LinearRegression
+
+def perform_regression(data, data_name, target_name):
+    X = data[data_name]
+    y = data[target_name]
+
+    if not isinstance(y.iloc[0], (int, float)):
+        raise ValueError("The target variable should be numeric (continuous) for regression.")
+
+    model = LinearRegression()
+    model.fit(X, y)
+    
+    return model
+
+def make_prediction(model, feature_names, input_values):
+    prediction = model.predict([input_values])
+
+    return prediction[0]

backend/visualization_strategy.py

@@ -0,0 +1,16 @@
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+def plot_histogram(data, column):
+    fig, ax = plt.subplots()
+    ax.hist(data[column].dropna(), bins=20, edgecolor='k')
+    ax.set_title(f"Histogram of {column}")
+    ax.set_xlabel(column)
+    ax.set_ylabel("Frequency")
+    return fig
+
+def plot_boxplot(data, column):
+    fig, ax = plt.subplots()
+    sns.boxplot(data=data, x=column, ax=ax)
+    ax.set_title(f"Boxplot of {column}")
+    return fig

frontend/clusters.py

@@ -1,83 +0,0 @@
-from sklearn.cluster import DBSCAN, KMeans
-import numpy as np
-from dataclasses import dataclass
-from abc import ABC, abstractmethod
-from typing import Any, Optional
-
-@dataclass
-class ClusterResult:
-    labels: np.array
-    centers: Optional[np.array]
-    statistics: list[dict[str, Any]]
-
-
-class Cluster(ABC):
-    @abstractmethod
-    def run(self, data: np.array) -> ClusterResult:
-        pass
-
-
-class DBSCANCluster(Cluster):
-    def __init__(self, eps: float = 0.5, min_samples: int = 5):
-        self.eps = eps
-        self.min_samples = min_samples
-
-    #@typing.override
-    def run(self, data: np.array) -> ClusterResult:
-        dbscan = DBSCAN(eps=self.eps, min_samples=self.min_samples)
-        labels = dbscan.fit_predict(data)
-        return ClusterResult(labels, None, self.get_statistics(data, labels))
-
-    def get_statistics(self, data: np.array, labels: np.array) -> list[dict[str, Any]]:
-        unique_labels = np.unique(labels)
-        stats = []
-        for label in unique_labels:
-            if label == -1:
-                continue
-            cluster_points = data[labels == label]
-            num_points = len(cluster_points)
-            density = num_points / (np.max(cluster_points, axis=0) - np.min(cluster_points, axis=0)).prod()
-            stats.append({
-                "cluster": label,
-                "num_points": num_points,
-                "density": density
-            })
-        return stats
-
-    def __str__(self) -> str:
-        return "DBScan"
-
-
-class KMeansCluster(Cluster):
-    def __init__(self, n_clusters: int = 8, n_init: int = 1, max_iter: int = 300):
-        self.n_clusters = n_clusters
-        self.n_init = n_init
-        self.max_iter = max_iter
-
-    #@typing.override
-    def run(self, data: np.array) -> ClusterResult:
-        kmeans = KMeans(n_clusters=self.n_clusters, init="random", n_init=self.n_init, max_iter=self.max_iter, random_state=111)
-        labels = kmeans.fit_predict(data)
-        centers = kmeans.cluster_centers_
-        return ClusterResult(labels, centers, self.get_statistics(data, labels, centers))
-
-    def get_statistics(self, data: np.array, labels: np.array, centers: np.array) -> list[dict[str, Any]]:
-        unique_labels = np.unique(labels)
-        stats = []
-
-        for label in unique_labels:
-            cluster_points = data[labels == label]
-            num_points = len(cluster_points)
-            center = centers[label]
-            stats.append({
-                "cluster": label,
-                "num_points": num_points,
-                "center": center,
-            })
-        return stats
-
-    def __str__(self) -> str:
-        return "KMeans"
-
-
-CLUSTERING_STRATEGIES = [DBSCANCluster(), KMeansCluster()]

frontend/pages/clustering.py

@@ -1,86 +0,0 @@
-import streamlit as st
-import matplotlib.pyplot as plt
-from clusters import DBSCANCluster, KMeansCluster, CLUSTERING_STRATEGIES
-from sklearn.decomposition import PCA
-from sklearn.metrics import silhouette_score
-import numpy as np
-
-st.header("Clustering")
-
-if "data" in st.session_state:
-    data = st.session_state.data
-
-    general_row = st.columns([1, 1, 1])
-    clustering = general_row[0].selectbox("Clustering method", CLUSTERING_STRATEGIES)
-    data_name = general_row[1].multiselect("Columns", data.select_dtypes(include="number").columns)
-    n_components = general_row[2].number_input("Reduce dimensions to (PCA)", min_value=1, max_value=3, value=2)
-
-    with st.form("cluster_form"):
-        if isinstance(clustering, KMeansCluster):
-            row1 = st.columns([1, 1, 1])
-            clustering.n_clusters = row1[0].number_input("Number of clusters", min_value=1, max_value=data.shape[0], value=clustering.n_clusters)
-            clustering.n_init = row1[1].number_input("n_init", min_value=1, value=clustering.n_init)
-            clustering.max_iter = row1[2].number_input("max_iter", min_value=1, value=clustering.max_iter)
-        elif isinstance(clustering, DBSCANCluster):
-            row1 = st.columns([1, 1])
-            clustering.eps = row1[0].slider("eps", min_value=0.0001, max_value=1.0, step=0.05, value=clustering.eps)
-            clustering.min_samples = row1[1].number_input("min_samples", min_value=1, value=clustering.min_samples)
-
-        st.form_submit_button("Launch")
-
-    if len(data_name) > 0:
-        x = data[data_name].to_numpy()
-        n_components = min(n_components, len(data_name))
-        if len(data_name) > n_components:
-            pca = PCA(n_components)
-            x = pca.fit_transform(x)
-            if n_components == 2:
-                (fig, ax) = plt.subplots(figsize=(8, 8))
-                for i in range(0, pca.components_.shape[1]):
-                    ax.arrow(
-                        0,
-                        0,
-                        pca.components_[0, i],
-                        pca.components_[1, i],
-                        head_width=0.1,
-                        head_length=0.1
-                    )
-
-                    plt.text(
-                        pca.components_[0, i] + 0.05,
-                        pca.components_[1, i] + 0.05,
-                        data_name[i]
-                    )
-                circle = plt.Circle((0, 0), radius=1, edgecolor='b', facecolor='None')
-                ax.add_patch(circle)
-                plt.axis("equal")
-                ax.set_title("PCA result - Correlation circle")
-                st.pyplot(fig)
-
-        result = clustering.run(x)
-        st.write("## Cluster stats")
-        st.table(result.statistics)
-
-        st.write("## Graphical representation")
-        fig = plt.figure()
-        if n_components == 1:
-            plt.scatter(x, np.zeros_like(x))
-        elif n_components == 2:
-            ax = fig.add_subplot(projection='rectilinear')
-            plt.scatter(x[:, 0], x[:, 1], c=result.labels, s=50, cmap="viridis")
-            if result.centers is not None:
-                plt.scatter(result.centers[:, 0], result.centers[:, 1], c="black", s=200, marker="X")
-        else:
-            ax = fig.add_subplot(projection='3d')
-
-            ax.scatter(x[:, 0], x[:, 1],x[:, 2], c=result.labels, s=50, cmap="viridis")
-            if result.centers is not None:
-                ax.scatter(result.centers[:, 0], result.centers[:, 1], result.centers[:, 2], c="black", s=200, marker="X")
-        st.pyplot(fig)
-        if not (result.labels == 0).all():
-            st.write("Silhouette score:", silhouette_score(x, result.labels))
-    else:
-        st.error("Select at least one column")
-
-else:
-    st.error("file not loaded")

frontend/pages/clustering_dbscan.py

@@ -0,0 +1,22 @@
+import streamlit as st
+import sys
+import os
+sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '../../backend')))
+from dbscan_strategy import perform_dbscan_clustering
+
+st.header("Clustering: DBSCAN")
+
+if "data" in st.session_state:
+    data = st.session_state.data
+
+    with st.form("dbscan_form"):
+        data_name = st.multiselect("Data Name", data.select_dtypes(include="number").columns, max_selections=3)
+        eps = st.slider("eps", min_value=0.0, max_value=1.0, value=0.5, step=0.01)
+        min_samples = st.number_input("min_samples", step=1, min_value=1, value=5)
+        submitted = st.form_submit_button("Launch")
+
+    if submitted and 2 <= len(data_name) <= 3:
+        fig = perform_dbscan_clustering(data, data_name, eps, min_samples)
+        st.pyplot(fig)
+else:
+    st.error("File not loaded")

frontend/pages/clustering_kmeans.py

@@ -0,0 +1,26 @@
+import streamlit as st
+import sys
+import os
+sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '../../backend')))
+from kmeans_strategy import perform_kmeans_clustering
+
+st.header("Clustering: KMeans")
+
+if "data" in st.session_state:
+    data = st.session_state.data
+
+    with st.form("kmeans_form"):
+        row1 = st.columns([1, 1, 1])
+        n_clusters = row1[0].selectbox("Number of clusters", range(1, data.shape[0]))
+        data_name = row1[1].multiselect("Data Name", data.select_dtypes(include="number").columns, max_selections=3)
+        n_init = row1[2].number_input("n_init", step=1, min_value=1)
+
+        row2 = st.columns([1, 1])
+        max_iter = row2[0].number_input("max_iter", step=1, min_value=1)
+        submitted = st.form_submit_button("Launch")
+
+    if submitted and 2 <= len(data_name) <= 3:
+        fig = perform_kmeans_clustering(data, data_name, n_clusters, n_init, max_iter)
+        st.pyplot(fig)
+else:
+    st.error("File not loaded")

frontend/pages/normalization.py

@@ -1,5 +1,8 @@
 import streamlit as st
-from normstrategy import MVStrategy, ScalingStrategy, KNNStrategy
+import sys
+import os
+sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '../../backend')))
+from norm_strategy import MVStrategy, ScalingStrategy, KNNStrategy
 
 if "data" in st.session_state:
     data = st.session_state.original_data

frontend/pages/prediction_classification.py

@@ -1,11 +1,8 @@
 import streamlit as st
-from sklearn.linear_model import LogisticRegression
-from sklearn.model_selection import train_test_split
-from sklearn.metrics import accuracy_score,confusion_matrix
-from sklearn.preprocessing import LabelEncoder
-import pandas as pd
-import matplotlib.pyplot as plt
-import seaborn as sns
+import sys
+import os
+sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '../../backend')))
+from classification_strategy import perform_classification, make_prediction
 
 st.header("Prediction: Classification")
 
@@ -14,66 +11,38 @@ if "data" in st.session_state:
 
     with st.form("classification_form"):
         st.subheader("Classification Parameters")
-        data_name = st.multiselect("Features", data.columns)
-        target_name = st.selectbox("Target", data.columns)
-        test_size = st.slider("Test Size", min_value=0.1, max_value=0.5, value=0.2, step=0.1)
-        st.form_submit_button('Train and Predict')
-
-    if data_name and target_name:
-        X = data[data_name]
-        y = data[target_name]
-
-        label_encoders = {}
-        for column in X.select_dtypes(include=['object']).columns:
-            le = LabelEncoder()
-            X[column] = le.fit_transform(X[column])
-            label_encoders[column] = le
-        
-        if y.dtype == 'object':
-            le = LabelEncoder()
-            y = le.fit_transform(y)
-            label_encoders[target_name] = le
-
-        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size, random_state=42)
-
-        model = LogisticRegression()
-        model.fit(X_train, y_train)
-        y_pred = model.predict(X_test)
-        accuracy = accuracy_score(y_test, y_pred)
-
+        data_name = st.multiselect("Features", data.columns, key="classification_features")
+        target_name = st.selectbox("Target", data.columns, key="classification_target")
+        test_size = st.slider("Test Size", min_value=0.1, max_value=0.5, value=0.2, step=0.1, key="classification_test_size")
+        submitted = st.form_submit_button('Train and Predict')
+
+    if submitted and data_name and target_name:
+        try:
+            model, label_encoders, accuracy = perform_classification(data, data_name, target_name, test_size)
+            st.session_state.classification_model = model
+            st.session_state.classification_label_encoders = label_encoders
+            st.session_state.classification_accuracy = accuracy
+            st.session_state.classification_features_selected = data_name
+            st.session_state.classification_target_selected = target_name
+        except ValueError as e:
+            st.error(e)
+
+    if "classification_model" in st.session_state:
         st.subheader("Model Accuracy")
-        st.write(f"Accuracy on test data: {accuracy:.2f}")
+        st.write(f"Accuracy on test data: {st.session_state.classification_accuracy:.2f}")
 
         st.subheader("Enter values for prediction")
-        pred_values = []
-        for feature in data_name:
-            if feature in label_encoders:
-                values = list(label_encoders[feature].classes_)
-                value = st.selectbox(f"Value for {feature}", values)
-                value_encoded = label_encoders[feature].transform([value])[0]
-                pred_values.append(value_encoded)
+        input_values = []
+        for feature in st.session_state.classification_features_selected:
+            if feature in st.session_state.classification_label_encoders:
+                values = list(st.session_state.classification_label_encoders[feature].classes_)
+                value = st.selectbox(f"Value for {feature}", values, key=f"classification_input_{feature}")
             else:
-                value = st.number_input(f"Value for {feature}", value=0.0)
-                pred_values.append(value)
-        
-        prediction = model.predict(pd.DataFrame([pred_values], columns=data_name))
-
-        if target_name in label_encoders:
-            prediction = label_encoders[target_name].inverse_transform(prediction)
-        
-        st.write("Prediction:", prediction[0])
-
-        if len(data_name) == 1:
-            fig = plt.figure()
-
-            y_pred = [model.predict(pd.DataFrame([pred_value[0]], columns=data_name)) for pred_value in X.values.tolist()]
-            cm = confusion_matrix(y, y_pred)
-
-            sns.heatmap(cm, annot=True, fmt="d")
+                value = st.number_input(f"Value for {feature}", value=0.0, key=f"classification_input_{feature}")
+            input_values.append(value)
 
-            plt.xlabel('Predicted')
-            plt.ylabel('True')
+        prediction = make_prediction(st.session_state.classification_model, st.session_state.classification_label_encoders, st.session_state.classification_features_selected, st.session_state.classification_target_selected, input_values)
         
-            st.pyplot(fig)
+        st.write("Prediction:", prediction)
 else:
     st.error("File not loaded")

frontend/pages/prediction_regression.py

@@ -1,8 +1,8 @@
 import streamlit as st
-from sklearn.linear_model import LinearRegression
-from sklearn.metrics import r2_score
-import pandas as pd
-import matplotlib.pyplot as plt
+import sys
+import os
+sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '../../backend')))
+from regression_strategy import perform_regression, make_prediction
 
 st.header("Prediction: Regression")
 
@@ -11,53 +11,24 @@ if "data" in st.session_state:
 
     with st.form("regression_form"):
         st.subheader("Linear Regression Parameters")
-        data_name = st.multiselect("Features", data.select_dtypes(include="number").columns)
-        target_name = st.selectbox("Target", data.select_dtypes(include="number").columns)
-        st.form_submit_button('Train and Predict')
-
-    if data_name and target_name:
-        X = data[data_name]
-        y = data[target_name]
-
-        model = LinearRegression()
-        model.fit(X, y)
-        
+        data_name = st.multiselect("Features", data.select_dtypes(include="number").columns, key="regression_features")
+        target_name = st.selectbox("Target", data.select_dtypes(include="number").columns, key="regression_target")
+        submitted = st.form_submit_button('Train and Predict')
+
+    if submitted and data_name and target_name:
+        try:
+            model = perform_regression(data, data_name, target_name)
+            st.session_state.regression_model = model
+            st.session_state.regression_features_selected = data_name
+            st.session_state.regression_target_selected = target_name
+        except ValueError as e:
+            st.error(e)
+
+    if "regression_model" in st.session_state:
         st.subheader("Enter values for prediction")
-        pred_values = [st.number_input(f"Value for {feature}", value=0.0) for feature in data_name]
-        prediction = model.predict(pd.DataFrame([pred_values], columns=data_name))
-
-        st.write("Prediction:", prediction[0])
-
-        fig = plt.figure()
-        dataframe_sorted = pd.concat([X, y], axis=1).sort_values(by=data_name)
-
-        if len(data_name) == 1:
-            y_pred = [model.predict(pd.DataFrame([pred_value[0]], columns=data_name)) for pred_value in X.values.tolist()]
-            r2 = r2_score(y, y_pred)
-            st.write('R-squared score:', r2)
-
-            X = dataframe_sorted[data_name[0]]
-            y = dataframe_sorted[target_name]
-
-            prediction_array_y = [
-                model.predict(pd.DataFrame([[dataframe_sorted[data_name[0]].iloc[i]]], columns=data_name))[0]
-                for i in range(dataframe_sorted.shape[0])
-            ]
-
-            plt.scatter(dataframe_sorted[data_name[0]], dataframe_sorted[target_name], color='b')
-            plt.plot(dataframe_sorted[data_name[0]], prediction_array_y, color='r')
-        elif len(data_name) == 2:
-            ax = fig.add_subplot(111, projection='3d')
-
-            prediction_array_y = [
-                model.predict(pd.DataFrame([[dataframe_sorted[data_name[0]].iloc[i], dataframe_sorted[data_name[1]].iloc[i]]], columns=data_name))[0]
-                for i in range(dataframe_sorted.shape[0])
-            ]
-
-            ax.scatter(dataframe_sorted[data_name[0]], dataframe_sorted[data_name[1]], dataframe_sorted[target_name], color='b')
-            ax.plot(dataframe_sorted[data_name[0]], dataframe_sorted[data_name[1]], prediction_array_y, color='r')
-
-        st.pyplot(fig)
+        input_values = [st.number_input(f"Value for {feature}", value=0.0, key=f"regression_input_{feature}") for feature in st.session_state.regression_features_selected]
+        prediction = make_prediction(st.session_state.regression_model, st.session_state.regression_features_selected, input_values)
 
+        st.write("Prediction:", prediction)
 else:
     st.error("File not loaded")

frontend/pages/visualization.py

@@ -1,30 +1,25 @@
 import streamlit as st
-import matplotlib.pyplot as plt
-import seaborn as sns
+import sys
+import os
+sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '../../backend')))
+from visualization_strategy import plot_histogram, plot_boxplot
 
 st.header("Data Visualization")
 
-
 if "data" in st.session_state:
     data = st.session_state.data
 
     st.subheader("Histogram")
     column_to_plot = st.selectbox("Select Column for Histogram", data.columns)
     if column_to_plot:
-        fig, ax = plt.subplots()
-        ax.hist(data[column_to_plot].dropna(), bins=20, edgecolor='k')
-        ax.set_title(f"Histogram of {column_to_plot}")
-        ax.set_xlabel(column_to_plot)
-        ax.set_ylabel("Frequency")
+        fig = plot_histogram(data, column_to_plot)
         st.pyplot(fig)
 
     st.subheader("Boxplot")
     dataNumeric = data.select_dtypes(include="number")
     column_to_plot = st.selectbox("Select Column for Boxplot", dataNumeric.columns)
     if column_to_plot:
-        fig, ax = plt.subplots()
-        sns.boxplot(data=data, x=column_to_plot, ax=ax)
-        ax.set_title(f"Boxplot of {column_to_plot}")
+        fig = plot_boxplot(data, column_to_plot)
         st.pyplot(fig)
 else:
     st.error("file not loaded")