Mise à jour de 'README.md'

Reviewed-on: #24

.drone.yml

@@ -0,0 +1,31 @@
+kind: pipeline
+type: docker
+name: Pow
+
+trigger:
+  event:
+    - push
+
+steps:
+
+  - name: build-pow
+    image: plugins/docker
+    settings:
+      dockerfile: ./src/Dockerfile
+      context: ./src
+      registry: hub.codefirst.iut.uca.fr
+      repo: hub.codefirst.iut.uca.fr/dorian.hodin/pow
+      username:
+        from_secret: SECRET_USERNAME
+      password:
+        from_secret: SECRET_PASSWD
+
+  - name: deploy-pow
+    image: hub.codefirst.iut.uca.fr/thomas.bellembois/codefirst-dockerproxy-clientdrone:latest
+    environment:
+      IMAGENAME: hub.codefirst.iut.uca.fr/dorian.hodin/pow:latest
+      CONTAINERNAME: pow
+      COMMAND: create
+      OVERWRITE: true
+      ADMINS: dorianhodin,aurianjault,remiarnal
+    depends_on: [ build-pow ]

.streamlit/config.toml

@@ -0,0 +1,2 @@
+[client]
+showSidebarNavigation = false

README.md

@@ -12,6 +12,13 @@ Projet de data mining, qui permet d'ouvrir un CSV, de pré-traiter et nettoyer l
 
 Dataset : https://catalog.data.gov/dataset/crash-reporting-drivers-data
 
+Lien du site déployé : https://codefirst.iut.uca.fr/containers/Picksteel-pow/ (ne pas oublier le /)
+
+Pour lancer le projet en local, depuis le dossier src faite :
+'''
+streamlit run /home.py
+'''
+ATTENTION : Ne pas run le ficher home.py depuis le dossier racine du projet, mais bien une fois dans le dossier src.
 
 # Développeurs 🧑‍💻
 

main.py

@@ -20,4 +20,4 @@ l.csv_value(df)
 
 # s.plotBoxWhisker(df)
 
-c.launch_cluster(df,['Speed Limit','Vehicle Year'])
+c.launch_cluster(df,['Speed Limit','Vehicle Year','Longitude'])

src/Dockerfile

@@ -0,0 +1,11 @@
+FROM python:3.9
+
+WORKDIR /app
+COPY . .
+
+RUN pip install --upgrade pip
+RUN pip install streamlit matplotlib pandas scikit-learn ydata-profiling
+
+EXPOSE 8080
+
+ENTRYPOINT ["streamlit", "run", "home.py", "--server.address=0.0.0.0", "--server.port=8080"]

src/back/clustering_csv.py

@@ -3,6 +3,7 @@ import matplotlib.pyplot as plt
 from sklearn.cluster import KMeans, DBSCAN
 from sklearn.datasets import make_blobs, make_moons
 from mpl_toolkits.mplot3d import Axes3D
+from sklearn.decomposition import PCA
 
 def visualize_clusters_2d(X, labels, centers=None, title="Clusters"):
     plt.figure(figsize=(10, 7))
@@ -12,7 +13,7 @@ def visualize_clusters_2d(X, labels, centers=None, title="Clusters"):
     plt.title(title)
     plt.xlabel("Feature 1")
     plt.ylabel("Feature 2")
-    plt.show()
+    return plt.gcf()
 
 def visualize_clusters_3d(X, labels, centers=None, title="Clusters"):
     fig = plt.figure(figsize=(10, 7))
@@ -24,7 +25,7 @@ def visualize_clusters_3d(X, labels, centers=None, title="Clusters"):
     ax.set_xlabel("Feature 1")
     ax.set_ylabel("Feature 2")
     ax.set_zlabel("Feature 3")
-    plt.show()
+    return plt.gcf()
 
 def calculate_cluster_statistics_kmeans(X, labels, centers):
     unique_labels = np.unique(labels)
@@ -56,7 +57,41 @@ def calculate_cluster_statistics_dbscan(X, labels):
         })
     return stats
 
-def launch_cluster(df,array_columns):
+def launch_cluster_knn(df, array_columns, n=3, dimensions=2):
+    X = df[array_columns].values
+    if len(array_columns) > 3:
+        pca = PCA(dimensions)
+        X = pca.fit_transform(df)
+    
+    kmeans = KMeans(n_clusters=n, random_state=42)
+    labels_kmeans = kmeans.fit_predict(X)
+    centers_kmeans = kmeans.cluster_centers_
+    # for stat in stats_kmeans:
+    #     print(f"Cluster {stat['cluster']}: {stat['num_points']} points, Center: {stat['center']}")
+
+    stats_kmeans = calculate_cluster_statistics_kmeans(X, labels_kmeans, centers_kmeans)
+    if dimensions == 3:
+        return visualize_clusters_3d(X, labels_kmeans, centers_kmeans, title="K-Means Clustering 3D")
+    else:
+        return visualize_clusters_2d(X, labels_kmeans, centers_kmeans, title="K-Means Clustering")
+
+def launch_cluster_dbscan(df, array_columns, dimensions=2):
+    X = df[array_columns].values
+    if len(array_columns) > 3:
+        pca = PCA(dimensions)
+        X = pca.fit_transform(df)
+    
+    dbscan = DBSCAN(eps=0.2, min_samples=5)
+    labels_dbscan = dbscan.fit_predict(X)
+    stats_dbscan = calculate_cluster_statistics_dbscan(X, labels_dbscan)
+    # for stat in stats_dbscan:
+    #     print(f"Cluster {stat['cluster']}: {stat['num_points']} points, Density: {stat['density']}")
+    if dimensions == 3:
+        return visualize_clusters_3d(X, labels_dbscan, title="DBSCAN Clustering 3D")
+    else:
+        return visualize_clusters_2d(X, labels_dbscan, title="DBSCAN Clustering")
+
+def launch_cluster(df, array_columns):
     X = df[array_columns].values
     
     kmeans = KMeans(n_clusters=4, random_state=42)

src/back/load_csv.py

@@ -1,6 +1,7 @@
 import pandas as pd
 import numpy  as np
 import matplotlib.pyplot as plt
+from sklearn.preprocessing import RobustScaler
 
 def return_csv(path):
     df = pd.read_csv(path)
@@ -13,7 +14,6 @@ def csv_value(df):
     print(df.isna().sum())
     # Useless values
 
-
 def csv_check(df):
     for col in df:
         print("-"*12)
@@ -21,28 +21,37 @@ def csv_check(df):
         print("-"*12)
         print(df[col].unique())
 
+def csv_norm_min_max(df, col):
+    max = df[col].max()
+    min = df[col].min()
+    df[col] = (df[col] - min)/ (max - min)
+    return df[col]
 
-def csv_norm_min_max(df,col):
-    maValue = df[col].max
-    miValue = df[col].min
-    df[col] = (df[col] - df[col].min()) / (df[col].max() - df[col].min())
-    return df
-
-def csv_standardisation_Z(df,col):
+def csv_standardisation_Z(df, col):
     mean_col1 = df[col].mean()
     std_col1 = df[col].std()
     df[col] = (df[col] - mean_col1) / std_col1
     return df[col]
 
-def csv_robust_normalize(df, column):
-    # Calcul de la médiane et de l'IQR
-    median = df[column].median()
-    q1 = df[column].quantile(0.25)
-    q3 = df[column].quantile(0.75)
-    iqr = q3 - q1
-
-    # Application de la normalisation robuste
-    normalized_column = (df[column] - median) / iqr
-    df[column] = normalized_column
-    print (normalized_column)
-    return normalized_column
+def robust_normalize_column(df, column_name):
+    # Extract the column datas
+    column_data = df[column_name].values.reshape(-1, 1)
+    
+    # Fit and transform the column datas
+    scaler = RobustScaler()
+    normalized_data = scaler.fit_transform(column_data)
+    df[column_name] = normalized_data
+    
+    return normalized_data
+
+def handle_normalization(df, norm_method):
+    for col_name in df:
+        if norm_method == "min-max":
+            df[col_name] = csv_norm_min_max(df, col_name)
+        elif norm_method == "z-score":
+            df[col_name] = csv_standardisation_Z(df, col_name)
+        elif norm_method == "robust":
+            df[col_name] = robust_normalize_column(df, col_name)
+        else:
+            raise ValueError("Unknown method")
+    return df

src/back/managing_missing_values.py

@@ -6,7 +6,9 @@ import load_csv as l
 
 def convert_categorical_to_numeric(data):
     for column in data.columns:
-        if data[column].nunique() <= 15:
+        if pd.api.types.is_numeric_dtype(data[column]):
+            continue
+        elif data[column].nunique() <= 15:
             data[column] = data[column].astype('category')
             data[column] = data[column].cat.codes.replace(-1, np.nan) + 1
         else:
@@ -18,7 +20,6 @@ def drop_high_null_percentage(data, threshold=0.5):
     data = data.loc[:, missing_percentage <= threshold]
     return data
 
-
 def replace_with_mean(data):
     return data.apply(lambda col: col.fillna(col.mean()) if col.dtype.kind in 'biufc' else col)
 
@@ -33,16 +34,24 @@ def impute_with_knn(data, n_neighbors=5):
     return pd.DataFrame(imputer.fit_transform(data), columns=data.columns)
 
 def impute_with_regression(data):
-    for column in data.columns:
-        if data[column].isnull().sum() > 0:
-            train_data = data[data[column].notna()]
-            test_data = data[data[column].isna()]
-            if not train_data.empty and not test_data.empty:
-                regressor = LinearRegression()
-                regressor.fit(train_data.drop(column, axis=1), train_data[column])
-                data.loc[data[column].isna(), column] = regressor.predict(test_data.drop(column, axis=1))
-    return data
+    missing_columns = data.columns[data.isnull().any()].tolist()
     
+    for col in missing_columns:
+        missing_data = data[data[col].isnull()]
+        complete_data = data[~data[col].isnull()]
+        if missing_data.empty or complete_data.empty:
+            continue
+        X_complete = complete_data.drop(columns=missing_columns)
+        y_complete = complete_data[col]
+        X_missing = missing_data.drop(columns=missing_columns)
+        if X_missing.shape[0] > 0.5 * data.shape[0]:
+            continue
+        model = LinearRegression()
+        model.fit(X_complete, y_complete)
+        y_pred = model.predict(X_missing)
+        data.loc[data[col].isnull(), col] = y_pred
+        
+    return data
 
 """    
     Parameters:
@@ -51,8 +60,6 @@ def impute_with_regression(data):
     - n_neighbors: Number of neighbors to use for KNN imputation (only used if method='knn')
 """
 def handle_missing_values(data, method, n_neighbors=5):
-
-    data = drop_high_null_percentage(data)
     data = convert_categorical_to_numeric(data)    
     if method == 'mean':
         return replace_with_mean(data)
@@ -66,4 +73,3 @@ def handle_missing_values(data, method, n_neighbors=5):
         return impute_with_regression(data)
     else:
         raise ValueError("Unknown method")
-

src/back/prediction.py

@@ -0,0 +1,37 @@
+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import LinearRegression
+from sklearn.ensemble import RandomForestRegressor
+from sklearn.metrics import f1_score
+from sklearn.metrics import accuracy_score
+import numpy as np
+import matplotlib.pyplot as plt
+
+def getColumnsForPredictionAndPredict(df,columns, columnGoal, algoOfPrediction):
+    predictors = df[columns]
+    target = df[columnGoal]
+
+    if algoOfPrediction == "Linear Regression":
+        model = LinearRegression()
+    elif algoOfPrediction == "Random Forest":
+        model = RandomForestRegressor(n_estimators=100)
+    else:
+        raise NameError("No method name : \"" + algoOfPrediction + "\"")
+
+    model.fit(predictors, target)
+    prediction = model.predict(predictors)
+    return prediction
+
+def correlation_matrix(df, columns):
+    new_df = df[columns]
+    correlations = new_df.corr()
+    print(correlations)
+    fig = plt.figure()
+    ax = fig.add_subplot(111)
+    cax = ax.matshow(correlations, vmin=-1, vmax=1)
+    fig.colorbar(cax)
+    ticks = np.arange(0,new_df.shape[1],1)
+    ax.set_xticks(ticks)
+    ax.set_yticks(ticks)
+    ax.set_xticklabels(list(new_df))
+    ax.set_yticklabels(list(new_df))
+    return fig

src/back/show_csv.py

@@ -2,15 +2,15 @@ import pandas as pd
 import numpy  as np
 import matplotlib.pyplot as plt
 
-def histo_col(df,colonne):
+def histo_col(df, col):
     plt.figure()
-    plt.hist(df[colonne], bins=int(df[colonne].nunique()/4), alpha=0.7, color='blue', edgecolor='black')
-    plt.title(f"Histogramme de la colonne '{colonne}'")
-    plt.xlabel(colonne)
+    plt.hist(df[col], bins=4, alpha=0.7, color='blue', edgecolor='black')
+    plt.title(f"Histogramme de la colonne '{col}'")
+    plt.xlabel(col)
     plt.ylabel("Fréquence")
     plt.grid(True)
-    plt.show()
+    return plt.gcf()
 
-def plotBoxWhisker(df):
-    df.plot(kind='box', subplots=True, sharex=False, sharey=False)
-    plt.show()
+def plotBoxWhisker(df, col):
+    df[col].plot(kind='box', subplots=True, sharex=False, sharey=False)
+    return plt.gcf()

src/home.py

@@ -0,0 +1,52 @@
+import streamlit as st
+from io import StringIO
+# from ydata_profiling import ProfileReport
+import pandas as pd
+
+def  statistics(df):
+    nan_counts = df.isnull().sum(axis=1).sum()
+
+    st.write("*Number of columns*:", len(df.columns))
+    st.write("*Number of rows*:", len(df.index))
+    
+    st.write("*Nan Counts*: ", nan_counts)
+    st.write(df.isna().sum())
+
+def display_df_first_and_lasts_lines(df):
+    fl = df.head(10)
+    ll = df.tail(10)
+    concat = pd.concat([fl, ll])
+    st.dataframe(concat)
+
+def nav_bar():
+    st.page_link("./home.py", label="Import", icon="⬆️", help=None)
+    st.page_link("pages/clean.py", label="Clean", icon="🧼", help=None)
+    st.page_link("pages/visualize.py", label="Visualize", icon="👁️", help=None)
+    st.page_link("pages/prediction.py", label="Predict", icon="🔮", help=None)
+
+def clean_dataframe(line):
+    # Call to function to clean data
+    line.empty()
+    line.write("Dataframe has been cleaned")
+
+def main():
+    nav_bar()
+    st.write("# Pow: Your data analyser")
+
+    uploaded_file = st.file_uploader("Choose a file")
+    if uploaded_file is not None:
+        df = pd.read_csv(uploaded_file)
+        st.session_state.original_df = df
+        st.write("## Dataframe (10 first/last lines)")
+        display_df_first_and_lasts_lines(df)
+
+        st.write("## Statistics")
+        statistics(df)
+
+        # profile = ProfileReport(df, title='Pandas Profiling Report', explorative=True)
+        # profile.to_widgets()
+
+        if st.button("Next"):
+            st.switch_page("pages/clean.py")
+
+main()

src/pages/clean.py

@@ -0,0 +1,43 @@
+import streamlit as st
+import sys
+sys.path.append('./back/')
+
+import managing_missing_values as mmv
+import load_csv as lc
+
+if 'original_df' in st.session_state:
+    df = st.session_state.original_df
+
+    st.write("# 🧼 Data cleaning")
+
+    st.write("## Missing data")
+    rm_empty_rows_or_cols = st.checkbox("Remove empty rows or columns", True)
+
+    st.write("#### Replace missing values")
+    replace_methods = ["mean","median","mode","knn","regression"]
+    replace_method = st.radio('Choose an option:', replace_methods)
+
+    st.write("## Normalize data")
+    normalize_methods = ["min-max","z-score","robust"]
+    normalize_method = st.radio('Choose an option:', normalize_methods)
+
+    is_cleaned = st.button("Clean dataset")
+    if is_cleaned:
+        if rm_empty_rows_or_cols:
+            st.write("-  Removing hight null percentage values")
+            df = mmv.drop_high_null_percentage(df)
+            st.dataframe(df)
+
+        st.write("- Handle missing values with method:", replace_method)
+        df = mmv.handle_missing_values(df, replace_method)
+        st.session_state.df = df
+        st.dataframe(df)
+
+        st.write("- Normalize with method:", normalize_method)
+        df = lc.handle_normalization(df, normalize_method)
+        st.session_state.df = df
+        st.dataframe(df)
+
+        st.switch_page("pages/visualize.py")
+else:
+    st.write("Please upload you dataset.")

src/pages/prediction.py

@@ -0,0 +1,74 @@
+import streamlit as st
+import pandas as pd
+import sys
+import matplotlib.pyplot as plt
+import numpy as np
+sys.path.append('./back/')
+
+import clustering_csv as cc
+import prediction as p
+
+def handle_column_multiselect(df, method_name):
+    selected_columns = st.multiselect(f"Select the columns you want for {method_name}:", df.columns.tolist(), placeholder="Select dataset columns")
+    return selected_columns
+    
+def df_prediction_results(df, targetCol, sourceColumns, method):
+    original_col = df[targetCol]
+    predicted_col = p.getColumnsForPredictionAndPredict(df, sourceColumns, targetCol, method)
+    
+    new_df = pd.DataFrame()
+    new_df['Original'] = original_col
+    new_df['Predicted'] = predicted_col
+
+    return new_df
+
+if 'df' in st.session_state:
+    df = st.session_state.df
+
+    st.write("# 🔮 Prediction")
+
+    tab1, tab2 = st.tabs(["Clustering", "Predictions"])
+
+    with tab1:
+        st.header("Clustering")
+        selected_columns = handle_column_multiselect(df, "clustering")
+
+        if len(selected_columns) >= 3:
+            dimensions = st.radio("Reduce to dimensions X with PCA:",[2,3],index=0)
+        else:
+            dimensions = 2
+        
+        tab_names = ["K-means", "DBSCAN"] 
+        cluster_tabs = st.tabs(tab_names)
+
+        for idx, tab in enumerate(cluster_tabs):
+            if tab.button(f"Start {tab_names[idx]}"):
+                if tab_names[idx] == "K-means":
+                    fig = cc.launch_cluster_knn(df, selected_columns, dimensions=dimensions)
+                else:
+                    fig = cc.launch_cluster_dbscan(df, selected_columns, dimensions)
+
+                tab.pyplot(fig)
+
+    with tab2:
+        st.header("Predictions")
+        target_column = st.selectbox(
+                            "Target column:",
+                            df.columns.tolist(),
+                            index=None,
+                            placeholder="Select target column"
+                        )
+
+        if target_column != None:
+            selected_columns_p = handle_column_multiselect(df, "predictions")
+        
+        tab_names = ["Linear Regression", "Random Forest"] 
+        prediction_tabs = st.tabs(tab_names)
+
+        for idx, tab in enumerate(prediction_tabs):
+            if tab.button(f"Start {tab_names[idx]}"):
+                tab.pyplot(p.correlation_matrix(df, selected_columns_p+[target_column]))
+                tmp_df = df_prediction_results(df, target_column, selected_columns_p, tab_names[idx])
+                tab.dataframe(tmp_df)
+else:
+    st.write("Please clean your dataset.")

src/pages/visualize.py

@@ -0,0 +1,32 @@
+import streamlit as st
+import matplotlib.pyplot as plt
+
+import sys
+sys.path.append('./back/')
+
+import show_csv as sc
+
+if 'df' in st.session_state:
+
+    df = st.session_state.df
+    df_columns = df.columns.tolist()
+
+    st.write("# 📊 Visualization")
+
+    st.write("## Histograms")
+    hist_tabs = st.tabs(df_columns)
+
+    for idx, tab in enumerate(hist_tabs):
+        tab.write("##### "+df_columns[idx])
+        tab.pyplot(sc.histo_col(df, df_columns[idx]))
+
+    st.write("## Box & Whisker")
+    baw_tabs = st.tabs(df_columns)
+
+    for idx, tab in enumerate(baw_tabs):
+        tab.write("##### "+df_columns[idx])
+        fig, ax = plt.subplots()
+        df[df_columns[idx]].plot(kind='box')
+        tab.pyplot(fig)
+else:
+    st.write('Please clean your dataset.')

transfusion.data

@@ -0,0 +1,749 @@
+Recency (months),Frequency (times),Monetary (c.c. blood),Time (months),"whether he/she donated blood in March 2007"
+2 ,50,12500,98 ,1
+0 ,13,3250,28 ,1
+1 ,16,4000,35 ,1
+2 ,20,5000,45 ,1
+1 ,24,6000,77 ,0
+4 ,4,1000,4 ,0
+2 ,7,1750,14 ,1
+1 ,12,3000,35 ,0
+2 ,9,2250,22 ,1
+5 ,46,11500,98 ,1
+4 ,23,5750,58 ,0
+0 ,3,750,4 ,0
+2 ,10,2500,28 ,1
+1 ,13,3250,47 ,0
+2 ,6,1500,15 ,1
+2 ,5,1250,11 ,1
+2 ,14,3500,48 ,1
+2 ,15,3750,49 ,1
+2 ,6,1500,15 ,1
+2 ,3,750,4 ,1
+2 ,3,750,4 ,1
+4 ,11,2750,28 ,0
+2 ,6,1500,16 ,1
+2 ,6,1500,16 ,1
+9 ,9,2250,16 ,0
+4 ,14,3500,40 ,0
+4 ,6,1500,14 ,0
+4 ,12,3000,34 ,1
+4 ,5,1250,11 ,1
+4 ,8,2000,21 ,0
+1 ,14,3500,58 ,0
+4 ,10,2500,28 ,1
+4 ,10,2500,28 ,1
+4 ,9,2250,26 ,1
+2 ,16,4000,64 ,0
+2 ,8,2000,28 ,1
+2 ,12,3000,47 ,1
+4 ,6,1500,16 ,1
+2 ,14,3500,57 ,1
+4 ,7,1750,22 ,1
+2 ,13,3250,53 ,1
+2 ,5,1250,16 ,0
+2 ,5,1250,16 ,1
+2 ,5,1250,16 ,0
+4 ,20,5000,69 ,1
+4 ,9,2250,28 ,1
+2 ,9,2250,36 ,0
+2 ,2,500,2 ,0
+2 ,2,500,2 ,0
+2 ,2,500,2 ,0
+2 ,11,2750,46 ,0
+2 ,11,2750,46 ,1
+2 ,6,1500,22 ,0
+2 ,12,3000,52 ,0
+4 ,5,1250,14 ,1
+4 ,19,4750,69 ,1
+4 ,8,2000,26 ,1
+2 ,7,1750,28 ,1
+2 ,16,4000,81 ,0
+3 ,6,1500,21 ,0
+2 ,7,1750,29 ,0
+2 ,8,2000,35 ,1
+2 ,10,2500,49 ,0
+4 ,5,1250,16 ,1
+2 ,3,750,9 ,1
+3 ,16,4000,74 ,0
+2 ,4,1000,14 ,1
+0 ,2,500,4 ,0
+4 ,7,1750,25 ,0
+1 ,9,2250,51 ,0
+2 ,4,1000,16 ,0
+2 ,4,1000,16 ,0
+4 ,17,4250,71 ,1
+2 ,2,500,4 ,0
+2 ,2,500,4 ,1
+2 ,2,500,4 ,1
+2 ,4,1000,16 ,1
+2 ,2,500,4 ,0
+2 ,2,500,4 ,0
+2 ,2,500,4 ,0
+4 ,6,1500,23 ,1
+2 ,4,1000,16 ,0
+2 ,4,1000,16 ,0
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