@jeremy had mentioned this might be happening. I would definitely love to see a machine learning forum created here to make it easier to discuss machine learning and the awesome lessons.
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I’m facing the below issue and tried couple things to fix but it doesn’t work -
a. tried to install graphviz pip install graphviz but it showed already installed .
b. added the path to system environment variables and restarted the notebook but still doesn’t work.
Can anyone please help me.
Thanks,
Sumit
pip does not install graphviz executable, you should download it yourself from https://www.graphviz.org/download/ or use conda conda install -c anaconda graphviz
Here is an attempt at waterfall plots with plotnine the ipynb codes cells follow.
This is still a work in progress any comments welcome
%load_ext autoreload
%autoreload 2
%matplotlib inline
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from plotnine import *
b0 = pd.DataFrame({'desc': ['sales','returns','credit fees','rebates','late charges','shipping'],
'amount': [350000,-30000,-7500,-25000,95000,-7000]})
def comma(x):
'The two args are the value '
if len(x) >1:
res = []
for el in x:
res.append("{:,.0f}".format(el))
else:
res = "{:,.0f}".format(x)
return res
def waterfall_df(balance):
"""
Expects a two column named 'amount' and 'desc' data frame
"""
balance.desc = pd.Categorical(balance.desc, categories=balance.desc)
balance['types'] = ["increase" if v > 0 else "decrease" for v in balance.amount]
total = balance.amount.sum()
balance = balance.append({'amount':total, 'desc':'net', 'types':'net'} , ignore_index=True)
balance = pd.concat([balance,pd.Series([v for v in range(balance.shape[0])])], axis=1 )
cols = balance.columns.values
cols[-1] = 'ind'
#print(cols, type(cols), balance.types.unique())
balance.columns = cols
#print(balance.amount.cumsum())
balance.types = pd.Categorical(balance.types, categories=['decrease', 'increase', 'net']) #balance.types.unique())
balance.iloc[0, len(cols) -2] = "net"
csum = balance.amount.cumsum()
zero_s = pd.Series([0.0],index=[len(csum)-1])
balance['end'] = csum[0:len(csum)-1].append(zero_s)
balance['start'] = csum[0:len(csum)].shift(1).fillna(0)
cmap = [ '#d83000' if v < 0 else '#242b73' for v in balance['amount']]
balance['cmap'] = cmap
return balance
def waterfall_plot(balance):
ind = balance.ind.values
end = balance.end.values
start = balance.start.values
end_lbl = comma(end)
start_lbl = comma(start)
nudge_end = [1 if e < s else -0.3 for e, s in zip(end,start)]
nudge_start = [-0.3 if e < s else 1 for e, s in zip(end,start)]
black = '#222222'
y_min = balance.end.values.min()
y_max = balance.end.values.max() + (0.2 * balance.end.values.max())
p1 = (ggplot(balance, aes('ind', fill = 'types')) +
geom_rect(aes(x = 'ind',xmin = ind - 0.45, xmax = ind + 0.45, ymin = end,ymax = start)) +
xlab("") +
ylab("") +
theme_seaborn() ) #+
#theme(
# axis_text = element_text(balance.desc, color='#555555', size=8, angle=45, va='bottom', margin={'t':10,'b':10})))
# axis_text_x=element_text(color=black)))
for s, e, i, t , a in zip(balance.start, balance.end, balance.ind, balance.types, balance.amount):
if t == 'increase' :
p1 = p1 + geom_text(
aes(x=i,y=e, label = a, nudge_y = 1), va='bottom', size = 8,format_string="{:,.0f}")
elif (t=='net') & (e > 0):
p1 = p1 + geom_text(
aes(x=i,y=e,label = a, nudge_y=nudge_end[0] ), va='bottom', size = 8, format_string="{:,.0f}")
elif (t=='net') & (s > 0):
p1 = p1 + geom_text(
aes(x=i,y=s, label = a, nudge_y = nudge_start[len(nudge_start)-1]),
va='bottom', size = 8,format_string="{:,.0f}")
elif t=='decrease':
p1 = p1 + geom_text(
aes(x=i,y=e, label = a, nudge_y = -0.3), va='top', size = 8,format_string="{:,.0f}")
p1 = p1 + geom_label(aes(y=y_max,label='desc'), color=black, size=8, angle=20, va='center')
#p1 = p1 + scale_fill_manual(values = [('decrease', "indianred"),('increase' ,"forestgreen"), ('net', "dodgerblue2")])
return p1
waterfall_plot(waterfall_df(b0))
try it on your data
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Are these videos enough to say we can start working on Machine Learning Models in real world ? Cna you please help me on it .
Sir , cannot thank you enough 
we could each create an initial model and crosscheck to see what we can learn from each other
should we choose a different dataset as houseprices has only 1461 samples for training ?
you could also email me at my username @ hotmail.com
sure …
Do you have any suggestions regarding a different dataset
Hi all, i tried building an image classifier based on lesson 1 of part 1.
I wrote my first medium post based on the results i had, please check it out and let me know your thoughts
I am getting an error that the kernel died when i execute the below code in lesson1 . Cna anyone please help me .
df, y, nas = proc_df(df_raw, ‘SalePrice’)
Can anyone help me with GBM and XGBOOST ?
Any lecture series or youtube videos ?
I had presented the “Ethics and Data Science” materials to my research group today, a 30 people data visualization group at HKUST! It was an underdiscussed issue, but not anymore! Thank you, @jeremy and @rachel !
Here is my slides, copied and annotated the original course slides on Github.
2 Likes
Hi,
In lesson4-mnist_sgd, I’m facing the below issue
Can anyone help me?
The earlier post was messed up with the reply to @sashank’s post !!
Thanks,
Sumit
Hi, people. Can someone help me on lesson 2 ? [Workbook 1]
I got this when running :
Please run this conda install -c anaconda graphviz .
Let me know if it doesn’t work.
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I’m facing the same issue but couldn’t able to solve it.
After looking into forums i ran this conda install -c defaults intel-openmp -f but nothing happend & also i don’t know what’s the significance of it.
Have you solved it? Can you please help me.
Thanks,
Sumit