def test_pie_like_px():
# Pie
labels = ["Oxygen", "Hydrogen", "Carbon_Dioxide", "Nitrogen"]
values = [4500, 2500, 1053, 500]
fig = px.pie(names=labels, values=values)
trace = go.Pie(labels=labels, values=values)
_compare_figures(trace, fig)
labels = [
"Eve", "Cain", "Seth", "Enos", "Noam", "Abel", "Awan", "Enoch", "Azura"
]
parents = ["", "Eve", "Eve", "Seth", "Seth", "Eve", "Eve", "Awan", "Eve"]
values = [10, 14, 12, 10, 2, 6, 6, 4, 4]
# Sunburst
fig = px.sunburst(names=labels, parents=parents, values=values)
trace = go.Sunburst(labels=labels, parents=parents, values=values)
_compare_figures(trace, fig)
# Treemap
fig = px.treemap(names=labels, parents=parents, values=values)
trace = go.Treemap(labels=labels, parents=parents, values=values)
_compare_figures(trace, fig)
# Funnel
x = ["A", "B", "C"]
y = [3, 2, 1]
fig = px.funnel(y=y, x=x)
trace = go.Funnel(y=y, x=x)
_compare_figures(trace, fig)
# Funnelarea
fig = px.funnel_area(values=y, names=x)
trace = go.Funnelarea(values=y, labels=x)
_compare_figures(trace, fig)
scatter_plot = go.Figure(
go.Scatter(x=[0, 1], y=[0, 1], mode="lines", name="ref"))
for al in metric_df.columns[1:].values:
AUC_val = auc(metric_dict[al]["ROC_Vals"][0].tolist(),
metric_dict[al]["ROC_Vals"][1].tolist())
scat = go.Scatter(x=metric_dict[al]["ROC_Vals"][0].tolist(),
y=metric_dict[al]["ROC_Vals"][1].tolist(),
name=f"{al} - AUC val - {AUC_val:.2f}")
scatter_plot.add_trace(scat)
scatter_plot.update_layout(width=1300, height=500)
st.header("ROC_curves")
st.plotly_chart(scatter_plot)
#-------------funnel-chart-----------------
st.header("Recommendations")
st.markdown(
"the percent below classifier represents recommended probability for classifier"
)
accu_dict = dict(
sorted(accu_dict.items(), key=lambda item: item[1], reverse=True))
funnel = go.Figure(
go.Funnelarea(values=list(accu_dict.values()),
text=list(accu_dict.keys())))
funnel.update_layout(showlegend=False)
st.plotly_chart(funnel)
# In[ ]:
import plotly.express as px
fig = px.funnel_area(names=["The 1st","The 2nd", "The 3rd", "The 4th", "The 5th"],
values=[5, 4, 3, 2, 1])
fig.show()
# In[ ]:
from plotly import graph_objects as go
fig = go.Figure(go.Funnelarea(
text = ["The 1st","The 2nd", "The 3rd", "The 4th", "The 5th"],
values = [5, 4, 3, 2, 1]
))
fig.show()
# In[ ]:
from plotly import graph_objects as go
fig = go.Figure(go.Funnelarea(
values = [5, 4, 3, 2, 1], text = ["The 1st","The 2nd", "The 3rd", "The 4th", "The 5th"],
marker = {"colors": ["deepskyblue", "lightsalmon", "tan", "teal", "silver"],
"line": {"color": ["wheat", "wheat", "blue", "wheat", "wheat"], "width": [0, 1, 5, 0, 4]}},
textfont = {"family": "Old Standard TT, serif", "size": 13, "color": "black"}, opacity = 0.65))
fig.show()
textposition="outside",
textinfo="value+percent previous"))
print('SHOW № 1: fig.show')
fig.show()
fig2 = go.Figure()
fig2.add_trace(
go.Funnelarea(
scalegroup="first",
values=values[3],
textinfo="value",
# textinfo=name_json,
title={
"position": "top center",
"text": "Current Week"
},
domain={
"x": [0, 0.5],
"y": [0, 0.5]
}))
fig2.add_trace(
go.Funnelarea(
scalegroup="first",
values=values[0],
textinfo="value",
# textinfo=name_json,
title={
"position": "top center",
def compute_class(data, target):
st.title("Classification Report")
#--------------------------------------preprocessing-------------------------------
drop_str = [col for col in data.columns if type(data[col][0]) == str]
data_head = data.copy(deep=True) #is used in presentation
data = data.drop(drop_str, axis=1)
data = data.drop(target.name, axis=1) #dropping y from data
pca = False # FIX THIS!
if len(data.columns) > 2:
pca = True
#------------------------------scaling------------------------
sc_1 = StandardScaler()
x_scaled = sc_1.fit_transform(data)
#----------------------------splits---------------------
x_train, x_test, y_train, y_test = train_test_split(x_scaled,
target,
test_size=0.2,
random_state=177013)
#-----------------------PCA only if >2 cols---------------
if pca == True:
pca = PCA(n_components=2)
x_train = pd.DataFrame(data=pca.fit_transform(x_train),
columns=['pc1', "pc2"]).iloc[:, :].values
x_test = pca.transform(x_test)
#----------------------------algorithms-----------------------------NB is disqualified
# (has some reservations about neg values)
classification_models = {
"LR": LogisticRegression(),
"SVC": SVC(kernel="rbf"),
"DTC": DecisionTreeClassifier(),
"RFC": RandomForestClassifier(n_estimators=500),
"XGBC": XGBClassifier(n_estimators=500)
}
metric_dict = {}
accu_dict = {}
for name, algorithm in tqdm(classification_models.items()):
model = algorithm
model.fit(x_train, y_train)
y_pred = model.predict(x_test)
metric_dict[name] = {
"precision":
round(
precision_score(y_test,
y_pred,
pos_label=y_pred[0],
average="micro"), 2),
"recall":
round(
recall_score(y_test,
y_pred,
pos_label=y_pred[0],
average="micro"), 2),
"f1_score":
round(f1_score(y_test, y_pred, average='micro'), 2),
"accuracy":
accuracy_score(y_test, y_pred),
"confusion":
confusion_matrix(y_test, y_pred),
"ROC_Vals":
roc_curve(y_test, y_pred, pos_label=y_pred[0])
}
accu_dict[name] = accuracy_score(y_test, y_pred)
#-------------------------helper FUNCTIONS---------------------
def list_maker(metric_dict, keyword="accuracy"):
key_list = list(metric_dict.keys())
return [metric_dict[key][keyword] for key in key_list]
def random_color(metric_dict):
return [
"#" +
''.join([random.choice('0123456789ABCDEF') for j in range(6)])
for i in range(len(metric_dict))
]
metric_df = pd.DataFrame(metric_dict).drop(["confusion", "ROC_Vals"],
axis=0)
metric_df.reset_index(inplace=True)
#--------------------------------------------- presentation and graphs -----------------------------------0
#-------------------------------------view data --------------------------
st.header("Lets look at what we are dealing with ")
st.dataframe(data_head.head())
#-----------------------------corelation_plot----------------------------------
st.header("Corelation Plot")
st.markdown("zoom if intelligible")
corr_val = data.corr()
corr = ff.create_annotated_heatmap(y=corr_val.index.tolist(),
x=corr_val.columns.tolist(),
z=corr_val.values)
for i in range(len(corr.layout.annotations)):
corr.layout.annotations[i].font.size = 8
corr.layout.annotations[i].text = str(
round(float(corr.layout.annotations[i].text), 4))
corr.update_layout(width=800, height=800)
st.plotly_chart(corr)
st.header("METRICS FOR CLASSIFICATION ALGORITHMS")
#------------------------metric_table-----------------
table = ff.create_table(metric_df)
table.update_layout(width=1350)
st.plotly_chart(table)
#--------------heatmaps------------------------------
st.markdown("### CONFUSION MATRICES")
fig = make_subplots(rows=1,
cols=len(metric_df.columns[1:].values),
shared_yaxes=True,
horizontal_spacing=0.05,
subplot_titles=metric_df.columns[1:].values)
annot_var = []
axis_count = 0
row_col = []
for row in range(1, 2):
for col in range(1, 6):
row_col.append([row, col])
row_col_pos = 0
for al in metric_df.columns[1:].values:
heatmap2 = ff.create_annotated_heatmap(
z=metric_dict[al]["confusion"],
x=["1_pred", "0_pred"],
y=["1_true", "0_true"],
annotation_text=metric_dict[al]["confusion"])
fig.add_trace(heatmap2.data[0], row_col[row_col_pos][0],
row_col[row_col_pos][1])
annot_temp = list(heatmap2.layout.annotations)
axis_count = axis_count + 1
row_col_pos = row_col_pos + 1
for k in range(len(annot_temp)):
annot_temp[k]['xref'] = "x" + str(axis_count)
annot_temp[k]['yref'] = 'y' + str(axis_count)
annot_var = annot_var + annot_temp
lo = list(fig['layout']["annotations"]) + annot_var
fig.update_layout(annotations=lo, autosize=True, width=1350)
st.plotly_chart(fig)
#------------scatter plots----------------
fpr, tpr, thres = roc_curve(y_test, y_pred, pos_label=y_pred[0])
scatter_plot = go.Figure(
go.Scatter(x=[0, 1], y=[0, 1], mode="lines", name="ref"))
for al in metric_df.columns[1:].values:
AUC_val = auc(metric_dict[al]["ROC_Vals"][0].tolist(),
metric_dict[al]["ROC_Vals"][1].tolist())
scat = go.Scatter(x=metric_dict[al]["ROC_Vals"][0].tolist(),
y=metric_dict[al]["ROC_Vals"][1].tolist(),
name=f"{al} - AUC val - {AUC_val:.2f}")
scatter_plot.add_trace(scat)
scatter_plot.update_layout(width=1300, height=500)
st.header("ROC_curves")
st.plotly_chart(scatter_plot)
#-------------funnel-chart-----------------
st.header("Recommendations")
st.markdown(
"the percent below classifier represents recommended probability for classifier"
)
accu_dict = dict(
sorted(accu_dict.items(), key=lambda item: item[1], reverse=True))
funnel = go.Figure(
go.Funnelarea(values=list(accu_dict.values()),
text=list(accu_dict.keys())))
funnel.update_layout(showlegend=False)
st.plotly_chart(funnel)
