def url_data():
about()
st.info("This feature has limited functionality")
url=st.text_input("Webpage URL",help="Enter a url where your data is placed")
if url=="":
st.info("Please enter a valid input to get started")
st.stop()
#getting data Column names as user input
column_name=st.text_input("enter candidadte column Name",key="value")
value_list=column_name.split(",")
#getting data example for refferances
candidate=st.text_input("Candidate example value",key="candidates",help="use ; as seperator to enter another value")
items_list=candidate.split(";")
#st.write(items)
# create object
scraper = AutoScraper()
# feeding for scraping
final_result = scraper.build(url,items_list)
# display result
results=scraper.get_result_similar(url,grouped=True,keep_order=True)
result={}
for key,value in results.items():
if value not in result.values():
result[key]=value
orient_df=pd.DataFrame.from_dict(result,orient="index")
df=orient_df.transpose()
df.columns=value_list
df.fillna(value=pd.np.nan,inplace=True)
st.write(df)
cols=df.columns.tolist()
col1,col2=st.beta_columns(2)
target=col1.selectbox("Select Target", cols,key="target")
typelist=['binary','multiclass','regression','time series regression','time series multiclass','time series binary']
p_type=col2.selectbox("Select problem type",typelist,key="p_type")
st.write("hey")
x=df.drop(columns=target)
y=df[target]
x_train,x_test,y_train,y_test=evalml.preprocessing.split_data(x,y,problem_type=p_type)
automl = AutoMLSearch(X_train=x_train, y_train=y_train, problem_type=p_type)
automl.search()
rank=automl.rankings
#checking best pipeline ###############################################################
best_pipeline=automl.best_pipeline
description=automl.describe_pipeline(automl.rankings.iloc[0]["id"])
### OPtimize the code
### Evaluate on hold out data
problem_list=['binary','time series binary']
problem_list2=['multiclass','time series multiclass']
cola,col_b,colc=st.beta_columns(3)
if p_type in problem_list:
objective=col_b.selectbox("select objective",objectives().binary_obj,key="objective selector")
best_pipeline.score(x_test, y_test, objectives=["auc","f1","Precision","Recall"])
automl_tunned = AutoMLSearch(X_train=x_train, y_train=y_train,
problem_type=p_type,
objective=objective,
additional_objectives=['f1', 'precision'],
max_batches=1,
optimize_thresholds=True)
automl_tunned.search()
tunned_rankings=automl_tunned.rankings
tunned_description=automl_tunned.describe_pipeline(automl_tunned.rankings.iloc[0]["id"],return_dict=True)
tunned_pipeline= automl_tunned.best_pipeline
tunned_pipeline.score(x_test, y_test, objectives=[objective])
pred=tunned_pipeline.predict_proba(x_test).to_dataframe()
# for multiclass type problem
elif p_type in problem_list2:
objective=col_b.selectbox("select objective",objectives().multiclass_obj,key="objective selector")
best_pipeline.score(x_test, y_test, objectives=["log loss multiclass","MCC multiclass","accuracy multiclass"])
automl_tunned = AutoMLSearch(X_train=x_train, y_train=y_train,
problem_type=p_type,
objective=objective,
additional_objectives=['MCC multiclass', 'accuracy multiclass'],
max_batches=1,
optimize_thresholds=True)
automl_tunned.search()
tunned_rankings=automl_tunned.rankings
tunned_description=automl_tunned.describe_pipeline(automl_tunned.rankings.iloc[0]["id"],return_dict=True)
tunned_pipeline= automl_tunned.best_pipeline
tunned_pipeline.score(x_test, y_test, objectives=[objective])
pred=tunned_pipeline.predict(x_test).to_series()
# for regression type problems
else:
objective=col_b.selectbox("select objective",objectives().regression_obj,key="objective selector")
best_pipeline.score(x_test, y_test, objectives=["r2","MSE","MAE","Root Mean Squared Error"])
automl_tunned = AutoMLSearch(X_train=x_train, y_train=y_train,
problem_type=p_type,
objective=objective,
additional_objectives=['Root Mean Squared Error', 'MSE','MAE'],
max_batches=1,
optimize_thresholds=True)
automl_tunned.search()
tunned_rankings=automl_tunned.rankings
tunned_description=automl_tunned.describe_pipeline(automl_tunned.rankings.iloc[0]["id"],return_dict=True)
tunned_pipeline= automl_tunned.best_pipeline
tunned_pipeline.score(x_test, y_test, objectives=[objective])
tunned_pipeline.fit(x_train,y_train)
pred=tunned_pipeline.predict(x_test).to_series()
file=open("model_details.txt","w")
str_dict=repr(tunned_description)
file.write(str_dict)
file.close()
def get_binary_file_downloader_html(bin_file, file_label='File'):
with open(bin_file, 'rb') as f:
data = f.read()
bin_str = base64.b64encode(data).decode()
href = f'<a href="data:application/octet-stream;base64,{bin_str}" download="{os.path.basename(bin_file)}">Click Here To Download {file_label}</a>'
return href
col1,col2,col3=st.beta_columns([1,1,1])
if col2.button("Predict Results",key="output",help="shows results"):
st.spinner()
with st.spinner(text='In progress'):
st.info("Wait while we are selecting a best algoritham for your problem..Hold your breath.")
time.sleep(20)
st.info("Done. Here you go.")
st.write(pred)
col11,col12=st.beta_columns([3,1])
with col11:
with st.beta_expander("Compare Models"):
st.write(tunned_rankings)
with col12:
with st.beta_expander("Best Pipeline"):
st.success(tunned_pipeline)
st.markdown(get_binary_file_downloader_html('model_details.txt', 'Pipeline Details'), unsafe_allow_html=True)
def file_data():
uploaded_file = st.file_uploader("Upload Files",type=['csv','xls','xlxs'])
#geeting the file type to read the file as a dataframe
if uploaded_file is None:
st.info("Upload a dataset first to start making predictions")
else:
filename=str(uploaded_file.name)
file_type=filename.split('.',2)
if file_type[1]=='csv':
df=pd.read_csv(uploaded_file)
elif file_type[1]=='xls' or 'xlsx':
df=pd.read_excel(uploaded_file)
st.write(df.head())
#wait for json type in new update
cols=df.columns.tolist()
col1,col2=st.beta_columns(2)
target=col1.selectbox("Select Target", cols,key="target")
x=df.drop(columns=target)
y=df[target]
typelist=['binary','multiclass','regression','time series regression','time series multiclass','time series binary']
p_type=col2.selectbox("Select problem type",typelist,key="p_type")
x_train,x_test,y_train,y_test=evalml.preprocessing.split_data(x,y,problem_type=p_type)
#its training time
automl = AutoMLSearch(X_train=x_train, y_train=y_train, problem_type=p_type)
automl.search()
rank=automl.rankings
#checking best pipeline ###############################################################
best_pipeline=automl.best_pipeline
description=automl.describe_pipeline(automl.rankings.iloc[0]["id"],return_dict=True)
### Evaluate on hold out data
problem_list=['binary','time series binary']
problem_list2=['multiclass','time series multiclass']
binary_obj=[]
for objective in get_core_objectives(ProblemTypes.BINARY):
binary_obj.append(objective.name)
multiclass_obj=[]
for objective in get_core_objectives(ProblemTypes.MULTICLASS):
multiclass_obj.append(objective.name)
regression_obj=[]
for objective in get_core_objectives(ProblemTypes.REGRESSION):
regression_obj.append(objective.name)
cola,col_b,colc=st.beta_columns([1,3,1])
if p_type in problem_list:
objective=col_b.selectbox("select objective",binary_obj,key="objective selector")
best_pipeline.score(x_test, y_test, objectives=["auc","f1","Precision","Recall"])
automl_tunned = AutoMLSearch(X_train=x_train, y_train=y_train,
problem_type=p_type,
objective=objective,
additional_objectives=['f1', 'precision'],
max_batches=1,
optimize_thresholds=True)
automl_tunned.search()
tunned_rankings=automl_tunned.rankings
tunned_description=automl_tunned.describe_pipeline(automl_tunned.rankings.iloc[0]["id"],return_dict=True)
tunned_pipeline= automl_tunned.best_pipeline
tunned_pipeline.score(x_test, y_test, objectives=[objective])
pred=tunned_pipeline.predict_proba(x_test).to_dataframe()
# for multiclass type problem
elif p_type in problem_list2:
objective=col_b.selectbox("select objective",multiclass_obj,key="objective selector")
best_pipeline.score(x_test, y_test, objectives=["log loss multiclass","MCC multiclass","accuracy multiclass"])
automl_tunned = AutoMLSearch(X_train=x_train, y_train=y_train,
problem_type=p_type,
objective=objective,
additional_objectives=['MCC multiclass', 'accuracy multiclass'],
max_batches=1,
optimize_thresholds=True)
automl_tunned.search()
tunned_rankings=automl_tunned.rankings
tunned_description=automl_tunned.describe_pipeline(automl_tunned.rankings.iloc[0]["id"],return_dict=True)
tunned_pipeline= automl_tunned.best_pipeline
tunned_pipeline.score(x_test, y_test, objectives=[objective])
pred=tunned_pipeline.predict(x_test).to_series()
# for regression type problems
else:
objective=col_b.selectbox("select objective",regression_obj,key="objective selector")
best_pipeline.score(x_test, y_test, objectives=["r2","MSE","MAE","Root Mean Squared Error"])
automl_tunned = AutoMLSearch(X_train=x_train, y_train=y_train,
problem_type=p_type,
objective=objective,
additional_objectives=['Root Mean Squared Error', 'MSE','MAE'],
max_batches=1,
optimize_thresholds=True)
automl_tunned.search()
tunned_rankings=automl_tunned.rankings
tunned_description=automl_tunned.describe_pipeline(automl_tunned.rankings.iloc[0]["id"],return_dict=True)
tunned_pipeline= automl_tunned.best_pipeline
tunned_pipeline.score(x_test, y_test, objectives=[objective])
tunned_pipeline.fit(x_train,y_train)
pred=tunned_pipeline.predict(x_test).to_series()
col1,col2,col3=st.beta_columns([1,3,1])
file=open("model_details.txt","w")
str_dict=repr(tunned_description)
file.write(str_dict)
file.close()
def get_binary_file_downloader_html(bin_file, file_label='File'):
with open(bin_file, 'rb') as f:
data = f.read()
bin_str = base64.b64encode(data).decode()
href = f'<a href="data:application/octet-stream;base64,{bin_str}" download="{os.path.basename(bin_file)}">Click Here To Download {file_label}</a>'
return href
if col2.button("Predict Results",key="output",help="shows results"):
st.spinner()
with st.spinner(text='In progress'):
st.info("Wait while we are selecting a best algoritham for your problem..Hold your breath.")
time.sleep(20)
st.info("Done. Here you go.")
st.write(pred)
col11,col12=st.beta_columns([3,1])
with col11:
with st.beta_expander("Compare Models"):
st.write(tunned_rankings)
with col12:
with st.beta_expander("Pipeline Details"):
st.success(tunned_pipeline)
st.markdown(get_binary_file_downloader_html('model_details.txt', 'Pipeline Details'), unsafe_allow_html=True)