ajout prediction classification

.gitignore

@@ -1,2 +1 @@
-__pycache__
 .venv

frontend/exploration.py

@@ -13,7 +13,6 @@ uploaded_file = st.file_uploader("Upload your CSV file", type=["csv"])
 
 if uploaded_file is not None:
     st.session_state.data = pd.read_csv(uploaded_file)
-    st.session_state.original_data = st.session_state.data
     st.success("File loaded successfully!")
 
 

frontend/normstrategy.py

@@ -1,138 +0,0 @@
-from abc import ABC, abstractmethod
-from pandas import DataFrame, Series
-from pandas.api.types import is_numeric_dtype
-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, 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()))
-        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 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/pages/clustering.py

@@ -0,0 +1,35 @@
+import streamlit as st
+from sklearn.cluster import KMeans
+import matplotlib.pyplot as plt
+
+st.header("Clustering")
+
+
+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, 10))
+        data_name = row1[1].multiselect("Data Name",data.select_dtypes(include="number").columns, max_selections=2)
+        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:
+        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, ax = plt.subplots(figsize=(12,8))
+        plt.scatter(x[:, 0], x[:, 1], s=100, c=kmeans.labels_, cmap='Set1')
+        plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1], s=400, marker='*', color='k')
+        st.pyplot(fig)
+
+else:
+    st.error("file not loaded")

frontend/pages/clustering_dbscan.py

@@ -1,35 +0,0 @@
-import streamlit as st
-import matplotlib.pyplot as plt
-from sklearn.cluster import DBSCAN
-
-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(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")
-        st.pyplot(fig)
-
-    
-
-else:
-    st.error("file not loaded")

frontend/pages/clustering_kmeans.py

@@ -1,44 +0,0 @@
-import streamlit as st
-from sklearn.cluster import KMeans
-import matplotlib.pyplot as plt
-
-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(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")
-        st.pyplot(fig)
-
-else:
-    st.error("file not loaded")

frontend/pages/normalization.py

@@ -1,32 +0,0 @@
-import streamlit as st
-from normstrategy import MVStrategy, ScalingStrategy
-
-if "data" in st.session_state:
-    data = st.session_state.original_data
-    st.session_state.original_data = data.copy()
-
-    for column, series in data.items():
-        col1, col2 = st.columns(2)
-        missing_count = series.isna().sum()
-        choices = MVStrategy.list_available(data, series)
-        option = col1.selectbox(
-            f"Missing values of {column} ({missing_count})",
-            choices,
-            index=1,
-            key=f"mv-{column}",
-        )
-        # Always re-get the series to avoid reusing an invalidated series pointer
-        data = option.apply(data, column, data[column])
-
-        choices = ScalingStrategy.list_available(data, series)
-        option = col2.selectbox(
-            "Scaling",
-            choices,
-            key=f"scaling-{column}",
-        )
-        data = option.apply(data, column, data[column])
-
-    st.write(data)
-    st.session_state.data = data
-else:
-    st.error("file not loaded")