import plotly
import plotly.graph_objs as go
#displays in a 3D space the model as a 3D mesh and the test set observations as a 3D scatter plot
def show3D(title, x_colname, y_colname, z_colname, c_colname, x_train, y_train, z_train, c_train, x_test, y_test, z_test, c_test, mesh_nb_pts, class_num, classifier):
n= int( mesh_nb_pts ** (1. / 3) ) #cubic root of mesh_nb_pts
min_x = min(x_train)
min_y = min(y_train)
min_z = min(z_train)
x_size = max(x_train) - min_x
y_size = max(y_train) - min_y
z_size = max(z_train) - min_z
x_step = x_size / n
y_step = y_size / n
z_step = z_size / n

#POSITIVE (1) PREDICTIONS AS A 3D MESH
i=0
x = np.empty([n*n*n])
y = np.empty([n*n*n])
z = np.empty([n*n*n])
for xi in range(0, n):
for yi in range(0, n):
for zi in range(0, n):
x[i] = min_x + xi * x_step
y[i] = min_y + yi * y_step
z[i] = min_z + zi * z_step
i = i + 1
print("Grid of size "+str(n)+"x"+str(n)+"x"+str(n)+" generated (nb vertices = "+str(n*n*n)+").")
#computing the predictions on the grid
datagrid = pd.DataFrame( {x_colname : x, y_colname: y, z_colname : z }) #the dataframe sort the columns lexicographically
datagrid = datagrid[[x_colname, y_colname, z_colname]] #fix the correct order of columns
p = classifier.predict(datagrid);
print("Predictions on the grid computed.")

#extracting the class_num-classified records from the predictions
ss = -1
if (class_num == 0):
ss = sum(1-p)
elif (class_num == 1):
ss = sum(p)
else:
print("ERROR. class_num=", class_num)

xx = np.empty([ss])
yy = np.empty([ss])
zz = np.empty([ss])
pp = np.empty([ss])
j = 0
for i in range(0, len(p)-1):
if(p[i] == class_num):
xx[j]=x[i]
yy[j]=y[i]
zz[j]=z[i]
pp[j]=p[i]
j = j + 1

print(str(ss)+" mesh coordinates extracted with ", class_num,"-predictions.")

#building the mesh for the predictions
trace_preds = go.Mesh3d(
x=xx,y=yy,z=zz,
alphahull=5, opacity=0.2, color='rgb(0, 256, 0)',
name = 'Predictions'
)
print("Mesh generated.")

#OBSERVATIONS 3D POINTS
#number of positive (1) and negative (0) predictions
s_pts = len(c_test)
s_pts_0 = 0
s_pts_1 = 0
for i in range(0, s_pts):
if (c_test[i] == 0) :
s_pts_0 = s_pts_0 + 1
elif (c_test[i] == 1) :
s_pts_1 = s_pts_1 + 1
else :
print("ERROR : c_test["+str(i)+"] = "+str(c_test[i]))
break

#building two set of points (x,y,z coordinates) : one for the positive (1) predictions and another one for the (0) negative predictions
x_pts_0 = np.empty([s_pts_0])
y_pts_0 = np.empty([s_pts_0])
z_pts_0 = np.empty([s_pts_0])
x_pts_1 = np.empty([s_pts_1])
y_pts_1 = np.empty([s_pts_1])
z_pts_1 = np.empty([s_pts_1])
j = 0
k = 0
for i in range(0, s_pts):
if (c_test[i] == 0) :
x_pts_0[j] = x_test[i]
y_pts_0[j] = y_test[i]
z_pts_0[j] = z_test[i]
j = j + 1
elif (c_test[i] == 1) :
x_pts_1[k] = x_test[i]
y_pts_1[k] = y_test[i]
z_pts_1[k] = z_test[i]
k = k + 1
else :
print("ERROR : c_test["+str(i)+"] = "+str(c_test[i]))
break

trace_obs_0 = go.Scatter3d(
x=x_pts_0, y=y_pts_0, z=z_pts_0,
mode='markers',
marker=dict(size=2, line=dict(color='rgb(128, 0, 0)', width=0.5), color='rgb(128, 0, 0)', opacity=1),
name='[red] Observations of class 0'
)

trace_obs_1 = go.Scatter3d(
x=x_pts_1, y=y_pts_1,z=z_pts_1,
mode='markers',
marker=dict(size=2, line=dict( color='rgba(0, 0, 0)', width=0.5 ), color='rgb(0, 0, 0)', opacity=1),
name='[black] Observations of class 1'
)

print("x from ", min_x, " to ", min_x+x_size/4)
print("y from ", min_y, " to ", min_y+y_size/4)
print("z from ", min_z, " to ", min_z+z_size/4)

layout = go.Layout(
title=title,
scene = dict(
xaxis=dict(title=x_colname, range = [min_x,min_x+x_size]),
yaxis=dict(title=y_colname, range = [min_y,min_y+y_size]),
zaxis=dict(title=z_colname, range = [min_z,min_z+z_size])
)
)

fig = go.Figure(data=[trace_obs_0, trace_obs_1, trace_preds], layout=layout)
plotly.offline.plot(fig)