delete :

backend/__init__.py

@@ -0,0 +1 @@
+from . import normstrategy

backend/normstrategy.py

@@ -0,0 +1,179 @@
+from abc import ABC, abstractmethod
+from pandas import DataFrame, Series
+from pandas.api.types import is_numeric_dtype
+from sklearn.neighbors import KNeighborsClassifier
+from typing import Any, Union
+
+class DataFrameFunction(ABC):
+    """A command that may be applied in-place to a dataframe."""
+
+    @abstractmethod
+    def apply(self, df: DataFrame, label: str, series: Series) -> DataFrame:
+        """Apply the current function to the given dataframe, in-place.
+
+        The series is described by its label and dataframe."""
+        return df
+
+
+class MVStrategy(DataFrameFunction):
+    """A way to handle missing values in a dataframe."""
+
+    @staticmethod
+    def list_available(df: DataFrame, label: str, series: Series) -> list['MVStrategy']:
+        """Get all the strategies that can be used."""
+        choices = [DropStrategy(), ModeStrategy()]
+        if is_numeric_dtype(series):
+            choices.extend((MeanStrategy(), MedianStrategy(), LinearRegressionStrategy()))
+            other_columns = df.select_dtypes(include="number").drop(label, axis=1).columns.to_list()
+            if len(other_columns):
+                choices.append(KNNStrategy(other_columns))
+        return choices
+
+
+class ScalingStrategy(DataFrameFunction):
+    """A way to handle missing values in a dataframe."""
+
+    @staticmethod
+    def list_available(df: DataFrame, series: Series) -> list['MVStrategy']:
+        """Get all the strategies that can be used."""
+        choices = [KeepStrategy()]
+        if is_numeric_dtype(series):
+            choices.extend((MinMaxStrategy(), ZScoreStrategy()))
+            if series.sum() != 0:
+                choices.append(UnitLengthStrategy())
+        return choices
+
+
+class DropStrategy(MVStrategy):
+    #@typing.override
+    def apply(self, df: DataFrame, label: str, series: Series) -> DataFrame:
+        df.dropna(subset=label, inplace=True)
+        return df
+
+    def __str__(self) -> str:
+        return "Drop"
+
+
+class PositionStrategy(MVStrategy):
+    #@typing.override
+    def apply(self, df: DataFrame, label: str, series: Series) -> DataFrame:
+        series.fillna(self.get_value(series), inplace=True)
+        return df
+
+    @abstractmethod
+    def get_value(self, series: Series) -> Any:
+        pass
+
+
+class MeanStrategy(PositionStrategy):
+    #@typing.override
+    def get_value(self, series: Series) -> Union[int, float]:
+        return series.mean()
+
+    def __str__(self) -> str:
+        return "Use mean"
+
+
+class MedianStrategy(PositionStrategy):
+    #@typing.override
+    def get_value(self, series: Series) -> Union[int, float]:
+        return series.median()
+
+    def __str__(self) -> str:
+        return "Use median"
+
+
+class ModeStrategy(PositionStrategy):
+    #@typing.override
+    def get_value(self, series: Series) -> Any:
+        return series.mode()[0]
+
+    def __str__(self) -> str:
+        return "Use mode"
+
+
+class LinearRegressionStrategy(MVStrategy):
+    def apply(self, df: DataFrame, label: str, series: Series) -> DataFrame:
+        series.interpolate(inplace=True)
+        return df
+
+    def __str__(self) -> str:
+        return "Use linear regression"
+
+
+class KNNStrategy(MVStrategy):
+    def __init__(self, training_features: list[str]):
+        self.available_features = training_features
+        self.training_features = training_features
+        self.n_neighbors = 3
+
+    def apply(self, df: DataFrame, label: str, series: Series) -> DataFrame:
+        # Remove any training column that have any missing values
+        usable_data = df.dropna(subset=self.training_features)
+        # Select columns to impute from
+        train_data = usable_data.dropna(subset=label)
+        # Create train dataframe
+        x_train = train_data.drop(label, axis=1)
+        y_train = train_data[label]
+
+        reg = KNeighborsClassifier(self.n_neighbors).fit(x_train, y_train)
+
+        # Create test dataframe
+        test_data = usable_data[usable_data[label].isnull()]
+        if test_data.empty:
+            return df
+        x_test = test_data.drop(label, axis=1)
+        predicted = reg.predict(x_test)
+
+        # Fill with predicated values and patch the original data
+        usable_data[label].fillna(Series(predicted), inplace=True)
+        df.fillna(usable_data, inplace=True)
+        return df
+
+    def count_max(self, df: DataFrame, label: str) -> int:
+        usable_data = df.dropna(subset=self.training_features)
+        return usable_data[label].count()
+
+    def __str__(self) -> str:
+        return "kNN"
+
+
+class KeepStrategy(ScalingStrategy):
+    #@typing.override
+    def apply(self, df: DataFrame, label: str, series: Series) -> DataFrame:
+        return df
+
+    def __str__(self) -> str:
+        return "No-op"
+
+
+class MinMaxStrategy(ScalingStrategy):
+    #@typing.override
+    def apply(self, df: DataFrame, label: str, series: Series) -> DataFrame:
+        minimum = series.min()
+        maximum = series.max()
+        df[label] = (series - minimum) / (maximum - minimum)
+        return df
+
+    def __str__(self) -> str:
+        return "Min-max"
+
+
+class ZScoreStrategy(ScalingStrategy):
+    #@typing.override
+    def apply(self, df: DataFrame, label: str, series: Series) -> DataFrame:
+        df[label] = (series - series.mean()) / series.std()
+        return df
+
+    def __str__(self) -> str:
+        return "Z-Score"
+
+
+class UnitLengthStrategy(ScalingStrategy):
+    #@typing.override
+    def apply(self, df: DataFrame, label: str, series: Series) -> DataFrame:
+        df[label] = series / series.sum()
+        return df
+
+    def __str__(self) -> str:
+        return "Unit length"

frontend/clusters.py

@@ -0,0 +1,63 @@
+from sklearn.cluster import DBSCAN, KMeans
+import numpy as np
+
+class DBSCAN_cluster():
+    def __init__(self, eps, min_samples,data):
+        self.eps = eps
+        self.min_samples = min_samples
+        self.data = data
+        self.labels = np.array([])
+
+    def run(self):
+        dbscan = DBSCAN(eps=self.eps, min_samples=self.min_samples)
+        self.labels = dbscan.fit_predict(self.data)
+        return self.labels
+
+    def get_stats(self):
+        unique_labels = np.unique(self.labels)
+        stats = []
+        for label in unique_labels:
+            if label == -1:
+                continue
+            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({
+                "cluster": label,
+                "num_points": num_points,
+                "density": density
+            })
+
+        return stats
+    
+
+class KMeans_cluster():
+    def __init__(self, n_clusters, n_init, max_iter, data):
+        self.n_clusters = n_clusters
+        self.n_init = n_init
+        self.max_iter = max_iter
+        self.data = data
+        self.labels = np.array([])
+        self.centers = []
+
+    def run(self):
+        kmeans = KMeans(n_clusters=self.n_clusters, init="random", n_init=self.n_init, max_iter=self.max_iter, random_state=111)
+        self.labels = kmeans.fit_predict(self.data)
+        self.centers = kmeans.cluster_centers_
+        return self.labels
+
+
+    def get_stats(self):
+        unique_labels = np.unique(self.labels)
+        stats = []
+
+        for label in unique_labels:
+            cluster_points = self.data[self.labels == label]
+            num_points = len(cluster_points)
+            center = self.centers[label]
+            stats.append({
+                'cluster': label,
+                'num_points': num_points,
+                'center': center
+            })
+        return stats

frontend/pages/clustering_dbscan.py

@@ -1,10 +1,9 @@
 import streamlit as st
 import matplotlib.pyplot as plt
-from sklearn.cluster import DBSCAN
+from clusters import DBSCAN_cluster
 
 st.header("Clustering: dbscan")
 
-
 if "data" in st.session_state:
     data = st.session_state.data
 
@@ -17,8 +16,9 @@ if "data" in st.session_state:
     if len(data_name) >= 2 and len(data_name) <=3:
         x = data[data_name].to_numpy()
 
-        dbscan = DBSCAN(eps=eps, min_samples=min_samples)
-        y_dbscan = dbscan.fit_predict(x)
+        dbscan = DBSCAN_cluster(eps,min_samples,x)
+        y_dbscan = dbscan.run()
+        st.table(dbscan.get_stats())
 
         fig = plt.figure()
         if len(data_name) == 2:
@@ -28,8 +28,5 @@ if "data" in st.session_state:
             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

@@ -1,10 +1,9 @@
 import streamlit as st
-from sklearn.cluster import KMeans
 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
 
@@ -23,21 +22,22 @@ if "data" in st.session_state:
     if len(data_name) >= 2 and len(data_name) <=3:
         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)
+        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")
-            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")
+            ax.scatter(centers[:, 0], centers[:, 1], centers[:, 2], c="black", s=200, marker="X")
         st.pyplot(fig)
 
 else: