add r2 score

frontend/clusters.py

@@ -1,40 +1,25 @@
 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 DBSCAN_cluster():
-class ClusterResult:
+    def __init__(self, eps, min_samples,data):
-    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
+        self.data = data
+        self.labels = np.array([])
 
-    #@typing.override
+    def run(self):
-    def run(self, data: np.array) -> ClusterResult:
         dbscan = DBSCAN(eps=self.eps, min_samples=self.min_samples)
-        labels = dbscan.fit_predict(data)
+        self.labels = dbscan.fit_predict(self.data)
-        return ClusterResult(labels, None, self.get_statistics(data, labels))
+        return self.labels
 
-    def get_statistics(self, data: np.array, labels: np.array) -> list[dict[str, Any]]:
+    def get_stats(self):
-        unique_labels = np.unique(labels)
+        unique_labels = np.unique(self.labels)
         stats = []
         for label in unique_labels:
             if label == -1:
                 continue
-            cluster_points = data[labels == label]
+            cluster_points = self.data[self.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({
@@ -42,42 +27,37 @@ class DBSCANCluster(Cluster):
                 "num_points": num_points,
                 "density": density
             })
+
         return stats
     
-    def __str__(self) -> str:
-        return "DBScan"
 
-
+class KMeans_cluster():
-class KMeansCluster(Cluster):
+    def __init__(self, n_clusters, n_init, max_iter, data):
-    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
+        self.data = data
+        self.labels = np.array([])
+        self.centers = []
 
-    #@typing.override
+    def run(self):
-    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)
+        self.labels = kmeans.fit_predict(self.data)
-        centers = kmeans.cluster_centers_
+        self.centers = kmeans.cluster_centers_
-        return ClusterResult(labels, centers, self.get_statistics(data, labels, centers))
+        return self.labels
 
-    def get_statistics(self, data: np.array, labels: np.array, centers: np.array) -> list[dict[str, Any]]:
+
-        unique_labels = np.unique(labels)
+    def get_stats(self):
+        unique_labels = np.unique(self.labels)
         stats = []
 
         for label in unique_labels:
-            cluster_points = data[labels == label]
+            cluster_points = self.data[self.labels == label]
             num_points = len(cluster_points)
-            center = centers[label]
+            center = self.centers[label]
             stats.append({
-                "cluster": label,
+                'cluster': label,
-                "num_points": num_points,
+                'num_points': num_points,
-                "center": center,
+                '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 +1,32 @@
+import streamlit as st
+import matplotlib.pyplot as plt
+from clusters import DBSCAN_cluster
+
+st.header("Clustering: dbscan")
+
+if "data" in st.session_state:
+    data = st.session_state.data
+
+    with st.form("my_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)
+        st.form_submit_button("launch")
+
+    if len(data_name) >= 2 and len(data_name) <=3:
+        x = data[data_name].to_numpy()
+
+        dbscan = DBSCAN_cluster(eps,min_samples,x)
+        y_dbscan = dbscan.run()
+        st.table(dbscan.get_stats())
+
+        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")
+        st.pyplot(fig)
+else:
+    st.error("file not loaded")

frontend/pages/clustering_kmeans.py

@@ -0,0 +1,44 @@
+import streamlit as st
+import matplotlib.pyplot as plt
+from clusters import KMeans_cluster
+
+st.header("Clustering: kmeans")
+
+if "data" in st.session_state:
+    data = st.session_state.data
+
+    with st.form("my_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 = row1[0].number_input("max_iter",step=1,min_value=1)
+
+
+        st.form_submit_button("launch")
+
+    if len(data_name) >= 2 and len(data_name) <=3:
+        x = data[data_name].to_numpy()
+
+        kmeans = KMeans_cluster(n_clusters, n_init, max_iter, x)
+        y_kmeans = kmeans.run()
+
+        st.table(kmeans.get_stats())
+
+        centers = kmeans.centers
+        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")
+            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")
+            ax.scatter(centers[:, 0], centers[:, 1], centers[:, 2], c="black", s=200, marker="X")
+        st.pyplot(fig)
+
+else:
+    st.error("file not loaded")

frontend/pages/prediction_classification.py

@@ -1,9 +1,11 @@
 import streamlit as st
 from sklearn.linear_model import LogisticRegression
 from sklearn.model_selection import train_test_split
-from sklearn.metrics import accuracy_score
+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
 
 st.header("Prediction: Classification")
 
@@ -60,5 +62,18 @@ if "data" in st.session_state:
             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")
+
+            plt.xlabel('Predicted')
+            plt.ylabel('True')
+
+            st.pyplot(fig, figsize=(1, 1))
 else:
     st.error("File not loaded")

frontend/pages/prediction_regression.py

@@ -1,6 +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
 
 st.header("Prediction: Regression")
 
@@ -25,5 +27,37 @@ if "data" in st.session_state:
         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)
+
 else:
     st.error("File not loaded")