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Picksteel:streamlit
Picksteel:data_treatment
Picksteel:deploy
Picksteel:prediction
Picksteel:managing_missing_values
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Picksteel:deploy
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Picksteel:managing_missing_values

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31
.drone.yml
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@ -1,31 +0,0 @@
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 ]

2
.gitignore vendored
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@ -159,4 +159,4 @@ cython_debug/
# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore # be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
# and can be added to the global gitignore or merged into this file. For a more nuclear # and can be added to the global gitignore or merged into this file. For a more nuclear
# option (not recommended) you can uncomment the following to ignore the entire idea folder. # option (not recommended) you can uncomment the following to ignore the entire idea folder.
#.idea/ #.idea/

2
.streamlit/config.toml
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@ -1,2 +0,0 @@
[client]
showSidebarNavigation = false

7
README.md
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@ -12,13 +12,6 @@ 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 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 🧑‍💻 # Développeurs 🧑‍💻

2
main.py
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@ -20,4 +20,4 @@ l.csv_value(df)
# s.plotBoxWhisker(df) # s.plotBoxWhisker(df)
c.launch_cluster(df,['Speed Limit','Vehicle Year','Longitude']) c.launch_cluster(df,['Speed Limit','Vehicle Year'])

11
src/Dockerfile
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@ -1,11 +0,0 @@
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"]

43
src/back/clustering_csv.py
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@ -3,7 +3,6 @@ import matplotlib.pyplot as plt
from sklearn.cluster import KMeans, DBSCAN from sklearn.cluster import KMeans, DBSCAN
from sklearn.datasets import make_blobs, make_moons from sklearn.datasets import make_blobs, make_moons
from mpl_toolkits.mplot3d import Axes3D from mpl_toolkits.mplot3d import Axes3D
from sklearn.decomposition import PCA
def visualize_clusters_2d(X, labels, centers=None, title="Clusters"): def visualize_clusters_2d(X, labels, centers=None, title="Clusters"):
plt.figure(figsize=(10, 7)) plt.figure(figsize=(10, 7))
@ -13,7 +12,7 @@ def visualize_clusters_2d(X, labels, centers=None, title="Clusters"):
plt.title(title) plt.title(title)
plt.xlabel("Feature 1") plt.xlabel("Feature 1")
plt.ylabel("Feature 2") plt.ylabel("Feature 2")
return plt.gcf() plt.show()
def visualize_clusters_3d(X, labels, centers=None, title="Clusters"): def visualize_clusters_3d(X, labels, centers=None, title="Clusters"):
fig = plt.figure(figsize=(10, 7)) fig = plt.figure(figsize=(10, 7))
@ -25,7 +24,7 @@ def visualize_clusters_3d(X, labels, centers=None, title="Clusters"):
ax.set_xlabel("Feature 1") ax.set_xlabel("Feature 1")
ax.set_ylabel("Feature 2") ax.set_ylabel("Feature 2")
ax.set_zlabel("Feature 3") ax.set_zlabel("Feature 3")
return plt.gcf() plt.show()
def calculate_cluster_statistics_kmeans(X, labels, centers): def calculate_cluster_statistics_kmeans(X, labels, centers):
unique_labels = np.unique(labels) unique_labels = np.unique(labels)
@ -57,41 +56,7 @@ def calculate_cluster_statistics_dbscan(X, labels):
}) })
return stats return stats
def launch_cluster_knn(df, array_columns, n=3, dimensions=2): def launch_cluster(df,array_columns):
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 X = df[array_columns].values
kmeans = KMeans(n_clusters=4, random_state=42) kmeans = KMeans(n_clusters=4, random_state=42)
@ -111,7 +76,7 @@ def launch_cluster(df, array_columns):
if len(array_columns) == 3: if len(array_columns) == 3:
visualize_clusters_3d(X, labels_kmeans, centers_kmeans, title="K-Means Clustering 3D") visualize_clusters_3d(X, labels_kmeans, centers_kmeans, title="K-Means Clustering 3D")
visualize_clusters_3d(X, labels_dbscan, title="DBSCAN Clustering 3D") visualize_clusters_3d(X, labels_dbscan, title="DBSCAN Clustering 3D")
else: else:
visualize_clusters_2d(X, labels_kmeans, centers_kmeans, title="K-Means Clustering") visualize_clusters_2d(X, labels_kmeans, centers_kmeans, title="K-Means Clustering")
visualize_clusters_2d(X, labels_dbscan, title="DBSCAN Clustering") visualize_clusters_2d(X, labels_dbscan, title="DBSCAN Clustering")
return stats_kmeans,stats_dbscan return stats_kmeans,stats_dbscan

49
src/back/load_csv.py
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@ -1,7 +1,6 @@
import pandas as pd import pandas as pd
import numpy as np import numpy as np
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
from sklearn.preprocessing import RobustScaler
def return_csv(path): def return_csv(path):
df = pd.read_csv(path) df = pd.read_csv(path)
@ -14,6 +13,7 @@ def csv_value(df):
print(df.isna().sum()) print(df.isna().sum())
# Useless values # Useless values
def csv_check(df): def csv_check(df):
for col in df: for col in df:
print("-"*12) print("-"*12)
@ -21,37 +21,28 @@ def csv_check(df):
print("-"*12) print("-"*12)
print(df[col].unique()) 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_standardisation_Z(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):
mean_col1 = df[col].mean() mean_col1 = df[col].mean()
std_col1 = df[col].std() std_col1 = df[col].std()
df[col] = (df[col] - mean_col1) / std_col1 df[col] = (df[col] - mean_col1) / std_col1
return df[col] return df[col]
def robust_normalize_column(df, column_name): def csv_robust_normalize(df, column):
# Extract the column datas # Calcul de la médiane et de l'IQR
column_data = df[column_name].values.reshape(-1, 1) median = df[column].median()
q1 = df[column].quantile(0.25)
# Fit and transform the column datas q3 = df[column].quantile(0.75)
scaler = RobustScaler() iqr = q3 - q1
normalized_data = scaler.fit_transform(column_data)
df[column_name] = normalized_data # Application de la normalisation robuste
normalized_column = (df[column] - median) / iqr
return normalized_data df[column] = normalized_column
print (normalized_column)
def handle_normalization(df, norm_method): return normalized_column
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

34
src/back/managing_missing_values.py
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@ -6,9 +6,7 @@ import load_csv as l
def convert_categorical_to_numeric(data): def convert_categorical_to_numeric(data):
for column in data.columns: for column in data.columns:
if pd.api.types.is_numeric_dtype(data[column]): if data[column].nunique() <= 15:
continue
elif data[column].nunique() <= 15:
data[column] = data[column].astype('category') data[column] = data[column].astype('category')
data[column] = data[column].cat.codes.replace(-1, np.nan) + 1 data[column] = data[column].cat.codes.replace(-1, np.nan) + 1
else: else:
@ -20,6 +18,7 @@ def drop_high_null_percentage(data, threshold=0.5):
data = data.loc[:, missing_percentage <= threshold] data = data.loc[:, missing_percentage <= threshold]
return data return data
def replace_with_mean(data): def replace_with_mean(data):
return data.apply(lambda col: col.fillna(col.mean()) if col.dtype.kind in 'biufc' else col) return data.apply(lambda col: col.fillna(col.mean()) if col.dtype.kind in 'biufc' else col)
@ -34,25 +33,17 @@ def impute_with_knn(data, n_neighbors=5):
return pd.DataFrame(imputer.fit_transform(data), columns=data.columns) return pd.DataFrame(imputer.fit_transform(data), columns=data.columns)
def impute_with_regression(data): def impute_with_regression(data):
missing_columns = data.columns[data.isnull().any()].tolist() for column in data.columns:
if data[column].isnull().sum() > 0:
for col in missing_columns: train_data = data[data[column].notna()]
missing_data = data[data[col].isnull()] test_data = data[data[column].isna()]
complete_data = data[~data[col].isnull()] if not train_data.empty and not test_data.empty:
if missing_data.empty or complete_data.empty: regressor = LinearRegression()
continue regressor.fit(train_data.drop(column, axis=1), train_data[column])
X_complete = complete_data.drop(columns=missing_columns) data.loc[data[column].isna(), column] = regressor.predict(test_data.drop(column, axis=1))
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 return data
""" """
Parameters: Parameters:
- data: Pandas DataFrame with the data - data: Pandas DataFrame with the data
@ -60,6 +51,8 @@ def impute_with_regression(data):
- n_neighbors: Number of neighbors to use for KNN imputation (only used if method='knn') - n_neighbors: Number of neighbors to use for KNN imputation (only used if method='knn')
""" """
def handle_missing_values(data, method, n_neighbors=5): def handle_missing_values(data, method, n_neighbors=5):
data = drop_high_null_percentage(data)
data = convert_categorical_to_numeric(data) data = convert_categorical_to_numeric(data)
if method == 'mean': if method == 'mean':
return replace_with_mean(data) return replace_with_mean(data)
@ -73,3 +66,4 @@ def handle_missing_values(data, method, n_neighbors=5):
return impute_with_regression(data) return impute_with_regression(data)
else: else:
raise ValueError("Unknown method") raise ValueError("Unknown method")

37
src/back/prediction.py
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@ -1,37 +0,0 @@
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

16
src/back/show_csv.py
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@ -2,15 +2,15 @@ import pandas as pd
import numpy as np import numpy as np
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
def histo_col(df, col): def histo_col(df,colonne):
plt.figure() plt.figure()
plt.hist(df[col], bins=4, alpha=0.7, color='blue', edgecolor='black') plt.hist(df[colonne], bins=int(df[colonne].nunique()/4), alpha=0.7, color='blue', edgecolor='black')
plt.title(f"Histogramme de la colonne '{col}'") plt.title(f"Histogramme de la colonne '{colonne}'")
plt.xlabel(col) plt.xlabel(colonne)
plt.ylabel("Fréquence") plt.ylabel("Fréquence")
plt.grid(True) plt.grid(True)
return plt.gcf() plt.show()
def plotBoxWhisker(df, col): def plotBoxWhisker(df):
df[col].plot(kind='box', subplots=True, sharex=False, sharey=False) df.plot(kind='box', subplots=True, sharex=False, sharey=False)
return plt.gcf() plt.show()

52
src/home.py
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@ -1,52 +0,0 @@
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()

43
src/pages/clean.py
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@ -1,43 +0,0 @@
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.")

74
src/pages/prediction.py
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@ -1,74 +0,0 @@
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.")

32
src/pages/visualize.py
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@ -1,32 +0,0 @@
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.')

749
transfusion.data
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@ -1,749 +0,0 @@
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
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