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import gradio as gr
import pandas as pd
import numpy as np
from sklearn.preprocessing import LabelEncoder
from sklearn.pipeline import Pipeline
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LinearRegression
from sklearn.svm import SVR
from sklearn.tree import DecisionTreeRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
def read(file,dep,ord):
df = pd.read_csv(file.name)
cat = list()
dep_type = str(df.dtypes[dep])
for col in df.columns.values:
if str(df.dtypes[col]) == 'bool' or str(df.dtypes[col]) == 'object':
cat.append(col)
new_df = df.dropna(axis=0)
if ord == "" and (dep_type == 'bool' or dep_type == 'object'):
ord = list()
ord.append(dep)
elif ord == "":
ord = list()
else:
pass
if len(ord)!=0:
le = LabelEncoder()
new_df[ord] = new_df[ord].apply(lambda col: le.fit_transform(col))
nom = list(set(cat).difference(set(ord)))
if len(nom) == 0:
pass
else:
ohe_df = pd.get_dummies(new_df[nom], drop_first=True)
new_df.drop(columns=nom, axis=1,inplace=True)
new_df = pd.concat([new_df,ohe_df],axis=1)
if dep_type == 'bool' or dep_type == 'object':
text = "classification"
result = classification(new_df,dep)
else:
text = "regression"
result = regression(new_df,dep)
return df.head(5),new_df.head(5),result, text, cat, ord, nom
def classification(df,dep):
X = df.drop(dep,axis=1)
y = df[dep]
X_train, X_test, y_train, y_test = train_test_split(X, y)
scale = StandardScaler()
pipe = Pipeline(steps=[('scale',scale),('classification','pass')])
parameters = [
{
'classification':[LogisticRegression()],
},
{
'classification':[RandomForestClassifier()],
},
{
'classification':[DecisionTreeClassifier()],
},
{
'classification':[SVC()],
},
{
'classification':[KNeighborsClassifier(n_neighbors=5)],
},
]
search = GridSearchCV(pipe, param_grid=parameters, n_jobs=-1, scoring='accuracy')
search.fit(X_train,y_train)
result = pd.DataFrame(search.cv_results_)[['params','rank_test_score','mean_test_score']]
result['mean_test_score']= (result['mean_test_score'])*100
result = result.astype({'params': str})
result.sort_values('rank_test_score',inplace=True)
return result
def regression(df,dep):
X = df.drop(dep,axis=1)
y =df[dep]
X_train, X_test, y_train, y_test = train_test_split(X, y)
scale = StandardScaler()
pipe = Pipeline(steps=[('scale',scale),('regression','pass')])
parameters = [
{
'regression':[LinearRegression()]
},
{
'regression':[RandomForestRegressor()],
},
{
'regression':[DecisionTreeRegressor()],
},
{
'regression':[SVR()],
},
]
search = GridSearchCV(pipe, param_grid=parameters, cv=5, n_jobs=-1, scoring='neg_mean_absolute_percentage_error')
search.fit(X_train,y_train)
result = pd.DataFrame(search.cv_results_)[['params','rank_test_score','mean_test_score']]
result['mean_test_score']= (result['mean_test_score']+1)*100
result = result.astype({'params': str})
result.sort_values('rank_test_score',inplace=True)
return result
with gr.Blocks() as demo:
gr.Markdown("Model Recommendation App **Upload** file to see the output.")
with gr.Column():
with gr.Row():
file = gr.File(label="Upload File(Comma Separated)")
dep = gr.Textbox(label="Dependent Variable(Variable as in the file)")
ord = gr.Textbox(label="Ordinal Variables(Seperate with a comma)")
submit = gr.Button("Submit")
text = gr.Text(label="Suitable Algorithm")
other1 = gr.Text(label="Categorical Variables")
other2 = gr.Text(label="LabelEncoded Vairables")
other3 = gr.Text(label="OneHotEncoded Variables")
with gr.Row():
org = gr.DataFrame(overflow_row_behaviour="paginate", label="Original Data")
converted = gr.DataFrame(overflow_row_behaviour="paginate", label="Transformed Data")
result = gr.DataFrame(label="Result")
submit.click(fn=read, inputs=[file,dep,ord], outputs=[org,converted,result,text,other1,other2,other3])
demo.launch()
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