markdown stringlengths 0 1.02M | code stringlengths 0 832k | output stringlengths 0 1.02M | license stringlengths 3 36 | path stringlengths 6 265 | repo_name stringlengths 6 127 |
|---|---|---|---|---|---|
Each variable plotted against loss: | plt.scatter('latent_dims', "valid_loss", data=df_res, marker="+", color='r')
plt.ylabel("Loss")
plt.xlabel("latent dimensions")
plt.ylim(16000, 70000)
plt.scatter('first_channel', "valid_loss", data=df_res, marker="+", color='r')
plt.ylabel("Loss")
plt.xlabel("First channel")
plt.ylim(16000, 80000)
plt.scatter('batch_n... | _____no_output_____ | MIT | notebooks/.ipynb_checkpoints/CAE_zoo_analysis-checkpoint.ipynb | scheng1992/Data_Assimilation |
Investigate "band" in loss-model plot Extract the different bands and inpsect | band1 = df_res[df_res.valid_loss < 20000]
band2 = df_res[(df_res.valid_loss > 20000) & (df_res.valid_loss < 23000)]
band3 = df_res[(df_res.valid_loss > 23000) & (df_res.valid_loss < 26000)]
band1.head()
band3.head() | _____no_output_____ | MIT | notebooks/.ipynb_checkpoints/CAE_zoo_analysis-checkpoint.ipynb | scheng1992/Data_Assimilation |
Investigate Duplicates | #eg1: /data/home/jfm1118/DA/experiments/CAE_zoo2/32 and /data/home/jfm1118/DA/experiments/CAE_zoo2/12
#eg2: /data/home/jfm1118/DA/experiments/CAE_zoo2/31 and /data/home/jfm1118/DA/experiments/CAE_zoo2/27
def get_data_from_path(path):
for res in results:
if res["path"] == path:
return res
els... | CAE2
ModuleList(
(0): Conv3d(1, 8, kernel_size=(3, 3, 3), stride=(1, 1, 1))
(1): Conv3d(8, 8, kernel_size=(3, 3, 2), stride=(2, 2, 2), padding=(1, 0, 0))
(2): Conv3d(8, 8, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 0))
(3): Conv3d(8, 8, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(0, 1, 0))
... | MIT | notebooks/.ipynb_checkpoints/CAE_zoo_analysis-checkpoint.ipynb | scheng1992/Data_Assimilation |
Investigate Best | path = "/data/home/jfm1118/DA/experiments/CAE_zoo2/17"
exp = get_data_from_path(base_exp + str(17))["settings"]
print_model(exp_1) | CAE2
ModuleList(
(0): Conv3d(1, 8, kernel_size=(3, 3, 3), stride=(1, 1, 1))
(1): Conv3d(8, 8, kernel_size=(3, 3, 2), stride=(2, 2, 2), padding=(1, 0, 0))
(2): Conv3d(8, 8, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 0))
(3): Conv3d(8, 8, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(0, 1, 0))
... | MIT | notebooks/.ipynb_checkpoints/CAE_zoo_analysis-checkpoint.ipynb | scheng1992/Data_Assimilation |
`np.tile` vs. `np.repeat` | np.tile([1, 2, 3], reps=2)
np.repeat([1, 2, 3], 2) | _____no_output_____ | MIT | lectures/11_Numpy_extra_stuff.ipynb | juditacs/bprof-python |
multidimensional | np.tile(np.repeat([1, 2, 3, 4], 2), 3)
d = {'b': 12}
dict({'a': 2}, **d)
a = np.arange(4).reshape(2, -1)
np.tile(a, (2, 3))
a = np.arange(4).reshape(2, -1)
np.repeat(a, (2, 5), axis=0)
a = np.arange(4).reshape(2, -1)
np.repeat(a, (2, 5), axis=1) | _____no_output_____ | MIT | lectures/11_Numpy_extra_stuff.ipynb | juditacs/bprof-python |
Set operations | a = np.array([1, 2, 3, 2, 3, 4, 3, 4, 5, 6])
b = np.array([7, 2, 10, 2, 7, 4, 9, 4, 9, 8])
np.intersect1d(a, b), np.setdiff1d(a, b) | _____no_output_____ | MIT | lectures/11_Numpy_extra_stuff.ipynb | juditacs/bprof-python |
Matching positions and elements | a = np.array([1, 2, 3, 2, 3, 4, 3, 4, 5, 6])
b = np.array([7, 2, 10, 2, 7, 4, 9, 4, 9, 8])
np.where(a == b), a[a==b] | _____no_output_____ | MIT | lectures/11_Numpy_extra_stuff.ipynb | juditacs/bprof-python |
Boolean indexing | a[a > 4] | _____no_output_____ | MIT | lectures/11_Numpy_extra_stuff.ipynb | juditacs/bprof-python |
Swapping columns | a = np.arange(10).reshape(2, -1)
a[:, [1, 2, 3, 0, 4]] | _____no_output_____ | MIT | lectures/11_Numpy_extra_stuff.ipynb | juditacs/bprof-python |
Standardizing and normalizingStandardizing: mean 0, std 1 | a = np.random.uniform(size=(5, 4), low=-5, high=10)
a
(a - a.mean()) / a.std() | _____no_output_____ | MIT | lectures/11_Numpy_extra_stuff.ipynb | juditacs/bprof-python |
Normalizing: squash into range [0, 1) | (a - a.min()) / a.ptp() | _____no_output_____ | MIT | lectures/11_Numpy_extra_stuff.ipynb | juditacs/bprof-python |
`np.digitize` | a = np.arange(1, 11).reshape(2, -1)
a = np.array([20, -2, 3, 5, 8, 7])
np.digitize(a, bins=[1, 4, 8]) | _____no_output_____ | MIT | lectures/11_Numpy_extra_stuff.ipynb | juditacs/bprof-python |
Local peaks | a = np.array([1, 3, 7, 1, 2, 6, 0, 1])
diff1 = a - np.hstack((a[1:], 0))
diff2 = a - np.hstack((0, a[:-1]))
np.where((diff1>0) & (diff2>0))
a = np.array([[3,3,3],[4,4,4],[5,5,5]])
b = np.array([1,2,3])
a - b[:, None]
x = np.array([1, 2, 1, 1, 3, 4, 3, 1, 1, 2, 1, 1, 2])
np.where(x == 1)[0][4] | _____no_output_____ | MIT | lectures/11_Numpy_extra_stuff.ipynb | juditacs/bprof-python |
Date range | np.arange(np.datetime64("2018-01-02"), np.datetime64("2018-01-15"), 3) | _____no_output_____ | MIT | lectures/11_Numpy_extra_stuff.ipynb | juditacs/bprof-python |
Strides | a = np.arange(15)
stride = 2
window = 4
np.array([a[i:i+window] for i in range(0, a.shape[0]-window+1, stride)]) | _____no_output_____ | MIT | lectures/11_Numpy_extra_stuff.ipynb | juditacs/bprof-python |
Trim digital signalTrim each consecutive block of ones to `min(cut, len(block))`. | import itertools
x = [0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1]
plt.step(np.arange(len(x)), x)
cut = 2
x = np.array([0] + x + [0])
up = np.where(np.diff(x) == 1)[0] + 1
down = np.where(np.diff(x) == -1)[0] + 1
delta = down - up
delta[delta > cut] = cut
x[:] = 0
x[list(itertools.chain(*(list(range... | _____no_output_____ | MIT | lectures/11_Numpy_extra_stuff.ipynb | juditacs/bprof-python |
Permutations | a = np.array([4, 3, 0, 10, 1])
order = np.argsort(-a)
a[order]
order, a[order][np.argsort(order)]
a = np.array([[1, -1, 2], [5, 0, 0]])
np.argmax(a, -1)
a.argmax(-1) | _____no_output_____ | MIT | lectures/11_Numpy_extra_stuff.ipynb | juditacs/bprof-python |
argsort | a = np.array([3, -1, 2, 0, 5, 2])
order = np.argsort(-a)
a[order]
a[order][np.argsort(order)]
[1, 2] * -1 | _____no_output_____ | MIT | lectures/11_Numpy_extra_stuff.ipynb | juditacs/bprof-python |
Welcome!Below, we will learn to implement and train a policy to play atari-pong, using only the pixels as input. We will use convolutional neural nets, multiprocessing, and pytorch to implement and train our policy. Let's get started! | # install package for displaying animation
!pip install JSAnimation
# custom utilies for displaying animation, collecting rollouts and more
import pong_utils
%matplotlib inline
# check which device is being used.
# I recommend disabling gpu until you've made sure that the code runs
device = pong_utils.device
print(... | List of available actions: ['NOOP', 'FIRE', 'RIGHT', 'LEFT', 'RIGHTFIRE', 'LEFTFIRE']
| MIT | pong/pong-PPO.ipynb | thomasgui76/deep-reinforcement-learning |
PreprocessingTo speed up training, we can simplify the input by cropping the images and use every other pixel | import matplotlib
import matplotlib.pyplot as plt
# show what a preprocessed image looks like
env.reset()
_, _, _, _ = env.step(0)
# get a frame after 20 steps
for _ in range(20):
frame, _, _, _ = env.step(1)
plt.subplot(1,2,1)
plt.imshow(frame)
plt.title('original image')
plt.subplot(1,2,2)
plt.title('preproces... | _____no_output_____ | MIT | pong/pong-PPO.ipynb | thomasgui76/deep-reinforcement-learning |
Policy Exercise 1: Implement your policy Here, we define our policy. The input is the stack of two different frames (which captures the movement), and the output is a number $P_{\rm right}$, the probability of moving left. Note that $P_{\rm left}= 1-P_{\rm right}$ | import torch
import torch.nn as nn
import torch.nn.functional as F
# set up a convolutional neural net
# the output is the probability of moving right
# P(left) = 1-P(right)
class Policy(nn.Module):
def __init__(self):
super(Policy, self).__init__()
########
##
## Modify y... | _____no_output_____ | MIT | pong/pong-PPO.ipynb | thomasgui76/deep-reinforcement-learning |
Game visualizationpong_utils contain a play function given the environment and a policy. An optional preprocess function can be supplied. Here we define a function that plays a game and shows learning progress | pong_utils.play(env, policy, time=200)
# try to add the option "preprocess=pong_utils.preprocess_single"
# to see what the agent sees | _____no_output_____ | MIT | pong/pong-PPO.ipynb | thomasgui76/deep-reinforcement-learning |
Function DefinitionsHere you will define key functions for training. Exercise 2: write your own function for training(what I call scalar function is the same as policy_loss up to a negative sign) PPOLater on, you'll implement the PPO algorithm as well, and the scalar function is given by$\frac{1}{T}\sum^T_t \min\left... | def clipped_surrogate(policy, old_probs, states, actions, rewards,
discount = 0.995, epsilon=0.1, beta=0.01):
########
##
## WRITE YOUR OWN CODE HERE
##
########
actions = torch.tensor(actions, dtype=torch.int8, device=device)
# convert states to policy (or prob... | _____no_output_____ | MIT | pong/pong-PPO.ipynb | thomasgui76/deep-reinforcement-learning |
TrainingWe are now ready to train our policy!WARNING: make sure to turn on GPU, which also enables multicore processing. It may take up to 45 minutes even with GPU enabled, otherwise it will take much longer! | from parallelEnv import parallelEnv
import numpy as np
# keep track of how long training takes
# WARNING: running through all 800 episodes will take 30-45 minutes
# training loop max iterations
episode = 500
# widget bar to display progress
!pip install progressbar
import progressbar as pb
widget = ['training loop: '... | _____no_output_____ | MIT | pong/pong-PPO.ipynb | thomasgui76/deep-reinforcement-learning |
Optimization Opt 1 parameter | def run(Plot, Save):
return
import numpy as np
from PyMieSim import Material
from PyMieSim.Scatterer import Sphere
from PyMieSim.Detector import Photodiode, LPmode
from PyMieSim.Source import PlaneWave
from PyMieSim.Experiment import ScatSet, SourceSet, Setup, DetectorSet
... | _____no_output_____ | MIT | docs/source/auto_examples/ExperimentOptimization/Optimization:Opt-1-parameter.ipynb | ejetzer/PyMieSim |
6. Hidden Markov Models with Theano and TensorFlowIn the last section we went over the training and prediction procedures of Hidden Markov Models. This was all done using only vanilla numpy the Expectation Maximization algorithm. I now want to introduce how both `Theano` and `Tensorflow` can be utilized to accomplish ... | import numpy as np
import theano
import theano.tensor as T
import seaborn as sns
import matplotlib.pyplot as plt
from hmm.utils import get_obj_s3, random_normalized
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
sns.set(style="white", palette="husl")
sns.set_context("talk")
sns.set_style("ticks")... | A learned from training:
[[0.50000007 0.49999993]
[0.50000005 0.49999995]]
B learned from training:
[[0.52666344 0.47333656]
[0.52666383 0.47333617]]
pi learned from training:
[0.50007189 0.49992811]
| MIT | Machine_Learning/05-Hidden_Markov_Models-06-Hidden-Markov-Models-Hidden-Markov-Models-Discrete-Observations-Deep-Learning-Libraries.ipynb | NathanielDake/NathanielDake.github.io |
3. HMM's with Theano $\rightarrow$ Optimization via SoftmaxOne of the challenges of the approach we took is that gradient descent is _unconstrained_; it simply goes in the direction of the gradient. This presents a problem for us in the case of HMM's. Remember, the parameters of an HMM are $\pi$, $A$, and $B$, and eac... | np.set_printoptions(suppress=True)
A = np.array([
[1,2],
[4,5],
[9,5]
])
expA = np.exp(A)
print("A exponentiated element wise: \n", np.round_(expA, decimals=3), "\n")
# Keep dims ensures a column vector (vs. row) output
output = expA / expA.sum(axis=1, keepdims=True)
print("Exponentiated A divided row s... | A exponentiated element wise:
[[ 2.718 7.389]
[ 54.598 148.413]
[8103.084 148.413]]
Exponentiated A divided row sum:
[[0.269 0.731]
[0.269 0.731]
[0.982 0.018]]
| MIT | Machine_Learning/05-Hidden_Markov_Models-06-Hidden-Markov-Models-Hidden-Markov-Models-Discrete-Observations-Deep-Learning-Libraries.ipynb | NathanielDake/NathanielDake.github.io |
Now you may be wondering: Why can't we just perform standard normalization? Why does the exponetial need to be used? For an answer to that I recommend reading up [here](https://stackoverflow.com/questions/17187507/why-use-softmax-as-opposed-to-standard-normalization), [here](https://stats.stackexchange.com/questions/16... | class HMM:
def __init__(self, M):
self.M = M
def fit(self, X, learning_rate=0.001, max_iter=10, V=None, p_cost=1.0, print_period=10):
"""Train HMM model using stochastic gradient descent."""
# Determine V, the vocabulary size
if V is None:
V = max(max(x) for x in X)... | _____no_output_____ | MIT | Machine_Learning/05-Hidden_Markov_Models-06-Hidden-Markov-Models-Hidden-Markov-Models-Discrete-Observations-Deep-Learning-Libraries.ipynb | NathanielDake/NathanielDake.github.io |
4. Hidden Markov Models with TensorFlowI now want to expose everyone to an HMM implementation in TensorFlow. In order to do so, we will need to first go over the `scan` function in Tensorflow. Just like when dealing with Theano, we need to ask "What is the equivalent of a for loop in TensorFlow?". And why should we ca... | import tensorflow as tf
x = tf.placeholder(tf.int32, shape=(None,), name="x")
def square(last, current):
"""Last is never used, but must be included based on interface requirements of tf.scan"""
return current*current
# Essentially doing what a for loop would normally do
# It applies the square function to e... | WARNING:tensorflow:From /Users/natedake/.virtualenvs/intuitiveml/lib/python3.6/site-packages/tensorflow/python/ops/tensor_array_ops.py:162: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Colocations handled automatically by placer.
... | MIT | Machine_Learning/05-Hidden_Markov_Models-06-Hidden-Markov-Models-Hidden-Markov-Models-Discrete-Observations-Deep-Learning-Libraries.ipynb | NathanielDake/NathanielDake.github.io |
Now, of course `scan` can do more complex things than this. We can implement another argument, `initializer`, that allows us to compute recurrence relationships. ```outputs = tf.scan( fn=some_function, Function applied to every element in sequence elems=thing_to_loop_over, Actual sequence that is pa... | # N is the number fibonacci numbers that we want
N = tf.placeholder(tf.int32, shape=(), name="N")
def fibonacci(last, current):
# last[0] is the last value, last[1] is the second last value
return (last[1], last[0] + last[1])
fib_op = tf.scan(
fn=fibonacci,
elems=tf.range(N),
initializer=(0, 1),
... | Output:
(array([ 1, 1, 2, 3, 5, 8, 13, 21], dtype=int32), array([ 1, 2, 3, 5, 8, 13, 21, 34], dtype=int32))
| MIT | Machine_Learning/05-Hidden_Markov_Models-06-Hidden-Markov-Models-Hidden-Markov-Models-Discrete-Observations-Deep-Learning-Libraries.ipynb | NathanielDake/NathanielDake.github.io |
Another example of what we can do with the theano `scan` is create a **low pass filter** (also known as a **moving average**). In this case, our recurrence relation is given by:$$s(t) = \text{decay_rate} \cdot s(t-1) + (1 - \text{decay_rate}) \cdot x(t)$$Where $x(t)$ is the input and $s(t)$ is the output. The goal here... | original = np.sin(np.linspace(0, 3*np.pi, 300))
X = 2*np.random.randn(300) + original
fig = plt.figure(figsize=(15,5))
plt.subplot(1, 2, 1)
ax = plt.plot(X, c="g", lw=1.5)
plt.title("Original")
# Setup placeholders
decay = tf.placeholder(tf.float32, shape=(), name="decay")
sequence = tf.placeholder(tf.float32, shape=... | _____no_output_____ | MIT | Machine_Learning/05-Hidden_Markov_Models-06-Hidden-Markov-Models-Hidden-Markov-Models-Discrete-Observations-Deep-Learning-Libraries.ipynb | NathanielDake/NathanielDake.github.io |
4.2 Discrete HMM With TensorflowLet's now take a moment to walk through the creation of a discrete HMM class utilizing Tensorflow. | import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from hmm.utils import get_obj_s3
class HMM:
def __init__(self, M):
self.M = M # number of hidden states
def set_session(self, session):
self.session = session
def fit(self, X, max_iter=10, print_period=1):
... | Number of train samples: 50
| MIT | Machine_Learning/05-Hidden_Markov_Models-06-Hidden-Markov-Models-Hidden-Markov-Models-Discrete-Observations-Deep-Learning-Libraries.ipynb | NathanielDake/NathanielDake.github.io |
Automagically making a table of all protein-protein interactions for two structuresIf two structures use the same or essentially the same, you can use Python to make a table of all the pairs of the protein-protein interactions by the two structures that can be used as input for the pipeline described in an earlier not... | structure1 = "6kiz"
structure2 = "6kix" | _____no_output_____ | MIT | notebooks/Automagically making a table of all protein-protein interactions for two structures.ipynb | fomightez/pdbsum-binder |
The next cell gets the script `pdb_code_to_prot_prot_interactions_via_PDBsum.py` (see [here](https://github.com/fomightez/structurework/tree/master/pdbsum-utilities)) that will get the 'Interface Summary' information for each individual structure. This is the equivalent to the Summary on the left side of the 'Prot-prot... | import os
file_needed = "pdb_code_to_prot_prot_interactions_via_PDBsum.py"
if not os.path.isfile(file_needed):
!curl -OL https://raw.githubusercontent.com/fomightez/structurework/master/pdbsum-utilities/{file_needed} | _____no_output_____ | MIT | notebooks/Automagically making a table of all protein-protein interactions for two structures.ipynb | fomightez/pdbsum-binder |
Import the main function of that script by running the next cell. | from pdb_code_to_prot_prot_interactions_via_PDBsum import pdb_code_to_prot_prot_interactions_via_PDBsum | _____no_output_____ | MIT | notebooks/Automagically making a table of all protein-protein interactions for two structures.ipynb | fomightez/pdbsum-binder |
The next cell gets the interaction summary for each structure and to get the pairs need to build the table described at the top of [Using snakemake to highlight changes in multiple protein-protein interactions via PDBsum data](Using%20snakemake%20to%20highlight%20changes%20in%20multiple%20protein-protein%20interactions... | structure1_il = pdb_code_to_prot_prot_interactions_via_PDBsum(structure1)
structure2_il = pdb_code_to_prot_prot_interactions_via_PDBsum(structure2)
i_union = set(structure1_il).union(set(structure2_il)) | _____no_output_____ | MIT | notebooks/Automagically making a table of all protein-protein interactions for two structures.ipynb | fomightez/pdbsum-binder |
In this case the pairs of both are the same; however, the script is written to not fail if there was extra proteins present in the other. Specficially, the interacting pairs of proteins for both are checked because if one had additional chain, by getting the listing of both structures and making the union, the combinat... | s = ""
for pair in list(i_union):
s+= f"{structure1} {pair[0]} {pair[1]} {structure2} {pair[0]} {pair[1]}\n"
%store s >int_matrix.txt | Writing 's' (str) to file 'int_matrix.txt'.
| MIT | notebooks/Automagically making a table of all protein-protein interactions for two structures.ipynb | fomightez/pdbsum-binder |
The table has now been stored as `int_matrix.txt`. Open the file from the Jupyter dashboard to verify. Or just run the next cell to see the contents of the file. | !cat int_matrix.txt | 6kiz K R 6kix K R
6kiz B H 6kix B H
6kiz C E 6kix C E
6kiz B D 6kix B D
6kiz G H 6kix G H
6kiz F H 6kix F H
6kiz D F 6kix D F
6kiz A N 6kix A N
6kiz N T 6kix N T
6kiz C D 6kix C D
6kiz A B 6kix A B
6kiz C F 6kix C F
6kiz K T 6kix K T
6kiz A E 6kix A E
6kiz A G 6kix A G
6kiz C K 6kix C K
6kiz E F 6kix E ... | MIT | notebooks/Automagically making a table of all protein-protein interactions for two structures.ipynb | fomightez/pdbsum-binder |
That's the table in the file that needed to be made. The rest of the process pickes up with 'Step 3' of [Using snakemake to highlight changes in multiple protein-protein interactions via PDBsum data](Using%20snakemake%20to%20highlight%20changes%20in%20multiple%20protein-protein%20interactions%20via%20PDBsum%20data.ipyn... | !snakemake --cores 1 | [33mBuilding DAG of jobs...[0m
[33mUsing shell: /bin/bash[0m
[33mProvided cores: 1 (use --cores to define parallelism)[0m
[33mRules claiming more threads will be scaled down.[0m
[33mJob counts:
count jobs
1 all
23 convert_scripts_to_nb_and_run_using_jupytext
1 make_archive
1 read_table_and_create_py
26[... | MIT | notebooks/Automagically making a table of all protein-protein interactions for two structures.ipynb | fomightez/pdbsum-binder |
Car Decor Sales Forecasting - Perfumes | Summary of the Code below :
1. Establish MySQL Connection and load data
2. Data Preprocessing (Typecasting and Resampling daily data to monthly)
3. Visualizing Rolling statistics to observe variation in mean and standard deviation for selected Feature.
4. Checking for Data Stationarity using Augmented Dickey-Fuller Tes... | _____no_output_____ | MIT | Model Buidling/Perfumes.ipynb | sangram6n/inventory_management_car-decors_sales_forecasting |
Importing Libraries | import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
from sklearn.metrics import mean_squared_error
from math import sqrt
# Connecting Python to MySQL for fetching data
import mysql.connector
import warnings
from statsmodels.tools.sm_exceptions import Conve... | _____no_output_____ | MIT | Model Buidling/Perfumes.ipynb | sangram6n/inventory_management_car-decors_sales_forecasting |
MySQL Connection to fetch data | try:
connection = mysql.connector.connect(host='localhost',
database='car_decors',
user='root',
password='***********')
sql_select_Query = "SELECT * FROM decorsales"
cursor = connection... | _____no_output_____ | MIT | Model Buidling/Perfumes.ipynb | sangram6n/inventory_management_car-decors_sales_forecasting |
Data Cleaning and Exploratory Data Analysis Converting fetched records to Pandas dataframe | records = np.array(records)
records = records[:,0:25]
decor_sales=pd.DataFrame(records,columns=columns) | _____no_output_____ | MIT | Model Buidling/Perfumes.ipynb | sangram6n/inventory_management_car-decors_sales_forecasting |
Type Casting Date and other features | decor_sales.dtypes
decor_sales.Date = pd.to_datetime(decor_sales.Date)
decor_sales.iloc[:,1:] = decor_sales.iloc[:,1:].astype("int32")
decor_sales.dtypes | _____no_output_____ | MIT | Model Buidling/Perfumes.ipynb | sangram6n/inventory_management_car-decors_sales_forecasting |
Creating Subset of Decor Sales Dataset and resampling Monthly Time Series | df = decor_sales
df = df.set_index('Date')
df = df.resample("MS").sum() | _____no_output_____ | MIT | Model Buidling/Perfumes.ipynb | sangram6n/inventory_management_car-decors_sales_forecasting |
Note : Time period options when resampling a time series MS - Monthly ; W - Weekly ; QS - Quarterly ; YS - Yearly | ###### Data Visualization | _____no_output_____ | MIT | Model Buidling/Perfumes.ipynb | sangram6n/inventory_management_car-decors_sales_forecasting |
plt.rc("figure", figsize=(16,8))sns.set_style('darkgrid') | ###### Rolling statistics to observe variation in mean and standard deviation. | _____no_output_____ | MIT | Model Buidling/Perfumes.ipynb | sangram6n/inventory_management_car-decors_sales_forecasting |
timeseries = df ['Perfumes']timeseries.rolling(12).mean().plot(label='12 Month Rolling Mean', marker='.')timeseries.rolling(12).std().plot(label='12 Month Rolling Std', marker='.')timeseries.plot(marker='.')plt.title('Rolling Statistics to observe variation in Mean and Standard Deviation', fontsize = 18, fontweight = '... | # The plot shows, there is nearly a constant mean and standard deviation except noise in Qtr 2 - 2020 (Lockdown period) | _____no_output_____ | MIT | Model Buidling/Perfumes.ipynb | sangram6n/inventory_management_car-decors_sales_forecasting |
Checking Seasonalty and Trend components for the feature | from statsmodels.tsa.seasonal import seasonal_decompose
add = seasonal_decompose(df["Perfumes"],model="additive",period=12)
add.plot(); | _____no_output_____ | MIT | Model Buidling/Perfumes.ipynb | sangram6n/inventory_management_car-decors_sales_forecasting |
Decomposition plot shows constant trend with noise in Qtr 2 - 2020 and seasonality is additive in nature. The data is seasonal and follows constant trend. Also, the average value or the mean of the residuals seem to be zero which holds our assumption. | ##### Checking for Data Stationarity using Augmented Dickey-Fuller Test | _____no_output_____ | MIT | Model Buidling/Perfumes.ipynb | sangram6n/inventory_management_car-decors_sales_forecasting |
from statsmodels.tsa.stattools import adfullerdef check_adf(time_series): test_result = adfuller(df['Perfumes']) print ('ADF Test:') labels = ['ADF Statistic','p-value','No. of Lags Used','Number of Observations Used'] for value,label in zip(test_result,labels): print (label+': '+str(value)+str("\n")... | If the data is non-stationary so we need to apply differencing to make our data stationary.
df ['Perfumes'] = df ['Perfumes'] - df ['Perfumes']. shift (1)
adf_check(df['Perfumes'].dropna())
If again data is non-stationary we need to differencing with subsequent shifts. | _____no_output_____ | MIT | Model Buidling/Perfumes.ipynb | sangram6n/inventory_management_car-decors_sales_forecasting |
check_adf(df['Perfumes']) | # Adfuller test Results for all variables | _____no_output_____ | MIT | Model Buidling/Perfumes.ipynb | sangram6n/inventory_management_car-decors_sales_forecasting |
from statsmodels.tsa.stattools import adfullerdef adfuller_parameter(x): P = [] columns = [] used_lag = [] for i in x.columns: test_stats,p,used_lags,nobs,critical_value,ic_best = adfuller(x[i]) columns.append(i) P.append(p) used_lag.append(used_lags) return pd.DataFrame({"COL... | By looking at adfuller test result we conclude that we need differencing by 0 shifts to make our data stationary for android headunits. | _____no_output_____ | MIT | Model Buidling/Perfumes.ipynb | sangram6n/inventory_management_car-decors_sales_forecasting |
Hyper-parameter Tuning Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) plots | # By looking at ACF pot and PACF plot we decide the value p(Auto regressive) and q(Moving average)
# p = sudden shuts off in pacf plot.
# q = Exponential drop in acf plot.
# d = degree of differencing/shift by adfuller test
#Auto Regressive (p)
# Identification of an AR model is often best done with the PACF.
# For an... | _____no_output_____ | MIT | Model Buidling/Perfumes.ipynb | sangram6n/inventory_management_car-decors_sales_forecasting |
from statsmodels.graphics.tsaplots import plot_acf, plot_pacfimport statsmodels.api as smfig, ax = plt.subplots(1,2, figsize=(15,5))sm.graphics.tsa.plot_acf(df["Perfumes"], lags=12, title = 'ACF Plot', ax=ax[0])sm.graphics.tsa.plot_pacf(df["Perfumes"], lags=12, title = 'PACF Plot',ax=ax[1])plt.show() | ### Model Building - SARIMA Model ( Seasonal ARIMA Model )
###### Train Test Split | _____no_output_____ | MIT | Model Buidling/Perfumes.ipynb | sangram6n/inventory_management_car-decors_sales_forecasting |
train_df = df["Perfumes"].iloc[0:int(len(df)*.95)] train model with approx 95% datatest_df = df["Perfumes"].iloc[int(len(train_df)):] test model with 5% dataprint("Train_df : ",len(train_df))print("Test_df : ",len(test_df)) | ###### User Defined Function to calculate the MAPE value | _____no_output_____ | MIT | Model Buidling/Perfumes.ipynb | sangram6n/inventory_management_car-decors_sales_forecasting |
def mape(y_true, y_pred): y_true, y_pred = np.array(y_true), np.array(y_pred) return np.mean(np.abs((y_true - y_pred) / y_true)) * 100 | ###### Automated Hyperparameter tuning | _____no_output_____ | MIT | Model Buidling/Perfumes.ipynb | sangram6n/inventory_management_car-decors_sales_forecasting |
import itertools as i p = range(0,3) d = range(0,2)q = range(0,3)pdq_combo = list(i.product(p,d,q)) this will all combination of p,d,q throgh a tuple error = []aic_sarima = []order_arima = []order_sarima = []seasonality = 12for pdq in pdq_combo: for PDQ in pdq_combo: try: SEASONAL_ORDER = list(PDQ... | ###### Model with best parameter | _____no_output_____ | MIT | Model Buidling/Perfumes.ipynb | sangram6n/inventory_management_car-decors_sales_forecasting |
sarima_model = sm.tsa.SARIMAX(train_df, order=(p_d_q), seasonal_order=(P_D_Q))sarima_results = sarima_model.fit(disp = 0)sarima_pred = sarima_results.predict(start=test_df.index[0],end=test_df.index[-1])sarima_pred_large = sarima_results.predict(start=75,end=86,dynamic=True) print(sarima_results.summary())sarima_diagno... | # Insights from these diagnostic plot :
# 1.The top left plot shows the residuals over time.
# The plot shows our residuals are fluctuating around mean 0 there is uniform deviation over time
# except some noise in second quarter of 2021 due to lockdown imposed by government with effect of COVID-19 pandemic.
# 2.In the... | _____no_output_____ | MIT | Model Buidling/Perfumes.ipynb | sangram6n/inventory_management_car-decors_sales_forecasting |
Predicted values Point estimationsarima_prediction = sarima_results.get_prediction(start = test_df.index[0], end = test_df.index[-1], dynamic = True, full_results = True)sarima_point_estimation = sarima_prediction.predicted_meansarima_point_estimation Checking MAPEmape(test_df, sarima_point_estimation) At 95% confide... | ############################################################################################################################ | _____no_output_____ | MIT | Model Buidling/Perfumes.ipynb | sangram6n/inventory_management_car-decors_sales_forecasting |
Holt Winters Exponential Smoothing with Additive Seasonality and Additive Trend | from statsmodels.tsa.seasonal import seasonal_decompose
from statsmodels.tsa.holtwinters import ExponentialSmoothing #
hwe_model_add_add = ExponentialSmoothing(train_df, seasonal ="add", trend = "add", seasonal_periods = 12).fit()
pred_hwe_add_add = hwe_model_add_add.predict(start = test_df.index[0], end = test_df.in... | _____no_output_____ | MIT | Model Buidling/Perfumes.ipynb | sangram6n/inventory_management_car-decors_sales_forecasting |
Plotting Holt Winters Model | plt.plot(train_df,color="g",label="Train Data")
plt.plot(test_df,color="b",label="Test Data")
plt.plot(pred_hwe_add_add,color="r",label="Forecast (Test Data)")
plt.suptitle('Model : Holt Winters', fontsize = 12, fontweight = 'bold')
plt.title('Car Decors - ANDROID HEAD UNITS', fontsize = 18, fontweight = 'bold')
plt.fi... | _____no_output_____ | MIT | Model Buidling/Perfumes.ipynb | sangram6n/inventory_management_car-decors_sales_forecasting |
### FB Prophet Model | _____no_output_____ | MIT | Model Buidling/Perfumes.ipynb | sangram6n/inventory_management_car-decors_sales_forecasting | |
Loading Librariesfrom fbprophet import Prophetfrom fbprophet.plot import plot_plotlydf1 = decor_salesdf1 = df1.set_index('Date')df1 = df1.resample("MS").sum()df1.reset_index(inplace=True) train_df1 = df1[["Date","Perfumes"]].iloc[0:int(len(df1)*.95)] train model with approx 95% datatest_df1 = df1[["Date","Perfumes"]].... | ############################################################################################################################ | _____no_output_____ | MIT | Model Buidling/Perfumes.ipynb | sangram6n/inventory_management_car-decors_sales_forecasting |
Auto Time Series Model | from auto_ts import auto_timeseries
train_df2 = train_df1
test_df2 = test_df1
ts_model = auto_timeseries( score_type='rmse', time_interval='MS', non_seasonal_pdq=(12,12,12), seasonality=True, seasonal_period=12, model_type="best", verbose=2)
ts_model.fit(traindata= train_df2, ts_column="ds", target="y")
ts_model.get_le... | _____no_output_____ | MIT | Model Buidling/Perfumes.ipynb | sangram6n/inventory_management_car-decors_sales_forecasting |
### Models Evaluation | _____no_output_____ | MIT | Model Buidling/Perfumes.ipynb | sangram6n/inventory_management_car-decors_sales_forecasting | |
from sklearn.metrics import mean_squared_error as mseprint("\nSARIMA Trend : ", p_d_q)print("SARIMA Seasonal Order : ", P_D_Q)print("SARIMA AIC : ", sarima_results.aic)print("SARIMA RMSE : ", np.sqrt(mse(test_df,sarima_point_estimation)))print("SARIMA MAPE : ", mape(test_df,... | ############################################################################################################################ | _____no_output_____ | MIT | Model Buidling/Perfumes.ipynb | sangram6n/inventory_management_car-decors_sales_forecasting |
Saving Model | import pickle
filename = 'sarima_model_perfumes.pkl'
pickle.dump(sarima_model, open(filename, 'wb')) | _____no_output_____ | MIT | Model Buidling/Perfumes.ipynb | sangram6n/inventory_management_car-decors_sales_forecasting |
Testing saved Model for prediction | ####### Model summary and diagnstics plot #######
with open(filename, "rb") as file:
load_model = pickle.load(file)
result = load_model.fit()
#print(result.summary())
#diagnostics = result.plot_diagnostics(figsize=(16,8))
pred = result.get_prediction(start = 76, end = 87, dynamic = False)
# Point estimation
p... | _____no_output_____ | MIT | Model Buidling/Perfumes.ipynb | sangram6n/inventory_management_car-decors_sales_forecasting |
Closing connection to MySQL and clearing variables from memory. | #if connection.is_connected():
# connection.close()
# cursor.close()
# print("MySQL connection is closed")
# Clear all variables from memory
#globals().clear()
##################################################################### | _____no_output_____ | MIT | Model Buidling/Perfumes.ipynb | sangram6n/inventory_management_car-decors_sales_forecasting |
Battle of the Neighbourhoods - Toronto Author: Ganesh ChunneThis notebook contains Questions 1, 2 & 3 of the Assignment. They have been segregated by Section headers | import pandas as pd | _____no_output_____ | Apache-2.0 | appliedDataScienceCapstoneWeek3.ipynb | Margchunne28/Location-data-provider |
Question 1 Importing Data | import requests
url = "https://en.wikipedia.org/wiki/List_of_postal_codes_of_Canada:_M"
wiki_url = requests.get(url)
wiki_url | _____no_output_____ | Apache-2.0 | appliedDataScienceCapstoneWeek3.ipynb | Margchunne28/Location-data-provider |
Response 200 means that we are able to make the connection to the page | wiki_data = pd.read_html(wiki_url.text)
wiki_data
len(wiki_data), type(wiki_data) | _____no_output_____ | Apache-2.0 | appliedDataScienceCapstoneWeek3.ipynb | Margchunne28/Location-data-provider |
We need the first table alone, so dropping the other tables | wiki_data = wiki_data[0]
wiki_data | _____no_output_____ | Apache-2.0 | appliedDataScienceCapstoneWeek3.ipynb | Margchunne28/Location-data-provider |
Dropping Borough which are not assigned | df = wiki_data[wiki_data["Borough"] != "Not assigned"]
df | _____no_output_____ | Apache-2.0 | appliedDataScienceCapstoneWeek3.ipynb | Margchunne28/Location-data-provider |
Grouping the records based on Postal Code | df = df.groupby(['Postal Code']).head()
df | _____no_output_____ | Apache-2.0 | appliedDataScienceCapstoneWeek3.ipynb | Margchunne28/Location-data-provider |
Checking for number of records where Neighbourhood is "Not assigned" | df.Neighbourhood.str.count("Not assigned").sum()
df = df.reset_index()
df
df.drop(['index'], axis = 'columns', inplace = True)
df
df.shape | _____no_output_____ | Apache-2.0 | appliedDataScienceCapstoneWeek3.ipynb | Margchunne28/Location-data-provider |
Answer to Question 1: We have 103 rows and 3 columns Question 2 Installing geocoder | pip install geocoder
import geocoder # import geocoder | _____no_output_____ | Apache-2.0 | appliedDataScienceCapstoneWeek3.ipynb | Margchunne28/Location-data-provider |
Tried the below approach, ran for 20 mins, then killed it. Changing the code cell to Text for now so that the run all execution doesn't stop. ```python initialize your variable to Nonelat_lng_coords = Nonepostal_code = 'M3A' loop until you get the coordinateswhile(lat_lng_coords is None): g = geocoder.google('{}, Toro... | data = pd.read_csv("https://cocl.us/Geospatial_data")
data
print("The shape of our wiki data is: ", df.shape)
print("the shape of our csv data is: ", data.shape) | The shape of our wiki data is: (103, 3)
the shape of our csv data is: (103, 3)
| Apache-2.0 | appliedDataScienceCapstoneWeek3.ipynb | Margchunne28/Location-data-provider |
Since the dimensions are the same, we can try to join on the postal codes to get the required data.Checking the column types of both the dataframes, especially Postal Code column since we are trying to join on it | df.dtypes
data.dtypes
combined_data = df.join(data.set_index('Postal Code'), on='Postal Code', how='inner')
combined_data
combined_data.shape | _____no_output_____ | Apache-2.0 | appliedDataScienceCapstoneWeek3.ipynb | Margchunne28/Location-data-provider |
**Solution:** We get 103 rows as expected when we do a inner join, so we have good data. Question 3 Drawing inspiration from the previous lab where we cluster the neighbourhood of NYC, We cluster Toronto based on the similarities of the venues categories using Kmeans clustering and Foursquare API. | from geopy.geocoders import Nominatim
address = 'Toronto, Ontario'
geolocator = Nominatim(user_agent="toronto_explorer")
location = geolocator.geocode(address)
latitude = location.latitude
longitude = location.longitude
print('The coordinates of Toronto are {}, {}.'.format(latitude, longitude)) | The coordinates of Toronto are 43.6534817, -79.3839347.
| Apache-2.0 | appliedDataScienceCapstoneWeek3.ipynb | Margchunne28/Location-data-provider |
Let's visualize the map of Toronto | import folium
# Creating the map of Toronto
map_Toronto = folium.Map(location=[latitude, longitude], zoom_start=11)
# adding markers to map
for latitude, longitude, borough, neighbourhood in zip(combined_data['Latitude'], combined_data['Longitude'], combined_data['Borough'], combined_data['Neighbourhood']):
label ... | _____no_output_____ | Apache-2.0 | appliedDataScienceCapstoneWeek3.ipynb | Margchunne28/Location-data-provider |
Initializing Foursquare API credentials | CLIENT_ID = '2GQBW5PR0QFXTOGCHKTRFWJBTGOFOHXW1TRTNRAFURQ5FE1X'
CLIENT_SECRET = '3QH40WMZIIDSQN1RFAVAEQHUIMOQUJPKYPABQVNTSDQJN2YD'
VERSION = 20202808
radius = 500
LIMIT = 100
print('Your credentails:')
print('CLIENT_ID: ' + CLIENT_ID)
print('CLIENT_SECRET:' + CLIENT_SECRET) | Your credentails:
CLIENT_ID: 2GQBW5PR0QFXTOGCHKTRFWJBTGOFOHXW1TRTNRAFURQ5FE1X
CLIENT_SECRET:3QH40WMZIIDSQN1RFAVAEQHUIMOQUJPKYPABQVNTSDQJN2YD
| Apache-2.0 | appliedDataScienceCapstoneWeek3.ipynb | Margchunne28/Location-data-provider |
Next, we create a function to get all the venue categories in Toronto | def getNearbyVenues(names, latitudes, longitudes):
venues_list=[]
for name, lat, lng in zip(names, latitudes, longitudes):
print(name)
# create the API request URL
url = 'https://api.foursquare.com/v2/venues/explore?&client_id={}&client_secret={}&v={}&ll={},{}&radius={}... | _____no_output_____ | Apache-2.0 | appliedDataScienceCapstoneWeek3.ipynb | Margchunne28/Location-data-provider |
Collecting the venues in Toronto for each Neighbourhood | venues_in_toronto = getNearbyVenues(combined_data['Neighbourhood'], combined_data['Latitude'], combined_data['Longitude'])
venues_in_toronto.shape | _____no_output_____ | Apache-2.0 | appliedDataScienceCapstoneWeek3.ipynb | Margchunne28/Location-data-provider |
So we have 1317 records and 5 columns. Checking sample data | venues_in_toronto.head() | _____no_output_____ | Apache-2.0 | appliedDataScienceCapstoneWeek3.ipynb | Margchunne28/Location-data-provider |
Checking the Venues based on Neighbourhood | venues_in_toronto.groupby('Neighbourhood').head() | _____no_output_____ | Apache-2.0 | appliedDataScienceCapstoneWeek3.ipynb | Margchunne28/Location-data-provider |
So there are 405 records for each neighbourhood.Checking for the maximum venue categories | venues_in_toronto.groupby('Venue Category').max() | _____no_output_____ | Apache-2.0 | appliedDataScienceCapstoneWeek3.ipynb | Margchunne28/Location-data-provider |
There are around 232 different types of Venue Categories. Interesting! One Hot encoding the venue Categories | toronto_venue_cat = pd.get_dummies(venues_in_toronto[['Venue Category']], prefix="", prefix_sep="")
toronto_venue_cat | _____no_output_____ | Apache-2.0 | appliedDataScienceCapstoneWeek3.ipynb | Margchunne28/Location-data-provider |
Adding the neighbourhood to the encoded dataframe | toronto_venue_cat['Neighbourhood'] = venues_in_toronto['Neighbourhood']
# moving neighborhood column to the first column
fixed_columns = [toronto_venue_cat.columns[-1]] + list(toronto_venue_cat.columns[:-1])
toronto_venue_cat = toronto_venue_cat[fixed_columns]
toronto_venue_cat.head() | _____no_output_____ | Apache-2.0 | appliedDataScienceCapstoneWeek3.ipynb | Margchunne28/Location-data-provider |
We will group the Neighbourhoods, calculate the mean venue categories in each Neighbourhood | toronto_grouped = toronto_venue_cat.groupby('Neighbourhood').mean().reset_index()
toronto_grouped.head() | _____no_output_____ | Apache-2.0 | appliedDataScienceCapstoneWeek3.ipynb | Margchunne28/Location-data-provider |
Let's make a function to get the top most common venue categories | def return_most_common_venues(row, num_top_venues):
row_categories = row.iloc[1:]
row_categories_sorted = row_categories.sort_values(ascending=False)
return row_categories_sorted.index.values[0:num_top_venues]
import numpy as np | _____no_output_____ | Apache-2.0 | appliedDataScienceCapstoneWeek3.ipynb | Margchunne28/Location-data-provider |
There are way too many venue categories, we can take the top 10 to cluster the neighbourhoods | num_top_venues = 10
indicators = ['st', 'nd', 'rd']
# create columns according to number of top venues
columns = ['Neighbourhood']
for ind in np.arange(num_top_venues):
try:
columns.append('{}{} Most Common Venue'.format(ind+1, indicators[ind]))
except:
columns.append('{}th Most Common Venue'.... | _____no_output_____ | Apache-2.0 | appliedDataScienceCapstoneWeek3.ipynb | Margchunne28/Location-data-provider |
Let's make the model to cluster our Neighbourhoods | # import k-means from clustering stage
from sklearn.cluster import KMeans
# set number of clusters
k_num_clusters = 5
toronto_grouped_clustering = toronto_grouped.drop('Neighbourhood', 1)
# run k-means clustering
kmeans = KMeans(n_clusters=k_num_clusters, random_state=0).fit(toronto_grouped_clustering)
kmeans | _____no_output_____ | Apache-2.0 | appliedDataScienceCapstoneWeek3.ipynb | Margchunne28/Location-data-provider |
Checking the labelling of our model | kmeans.labels_[0:100] | _____no_output_____ | Apache-2.0 | appliedDataScienceCapstoneWeek3.ipynb | Margchunne28/Location-data-provider |
Let's add the clustering Label column to the top 10 common venue categories | neighborhoods_venues_sorted.insert(0, 'Cluster Labels', kmeans.labels_) | _____no_output_____ | Apache-2.0 | appliedDataScienceCapstoneWeek3.ipynb | Margchunne28/Location-data-provider |
Join toronto_grouped with combined_data on neighbourhood to add latitude & longitude for each neighborhood to prepare it for plotting | toronto_merged = combined_data
toronto_merged = toronto_merged.join(neighborhoods_venues_sorted.set_index('Neighbourhood'), on='Neighbourhood')
toronto_merged.head() | _____no_output_____ | Apache-2.0 | appliedDataScienceCapstoneWeek3.ipynb | Margchunne28/Location-data-provider |
Drop all the NaN values to prevent data skew | toronto_merged_nonan = toronto_merged.dropna(subset=['Cluster Labels']) | _____no_output_____ | Apache-2.0 | appliedDataScienceCapstoneWeek3.ipynb | Margchunne28/Location-data-provider |
Plotting the clusters on the map | import matplotlib.cm as cm
import matplotlib.colors as colors
map_clusters = folium.Map(location=[latitude, longitude], zoom_start=11)
# set color scheme for the clusters
x = np.arange(k_num_clusters)
ys = [i + x + (i*x)**2 for i in range(k_num_clusters)]
colors_array = cm.rainbow(np.linspace(0, 1, len(ys)))
rainbow =... | _____no_output_____ | Apache-2.0 | appliedDataScienceCapstoneWeek3.ipynb | Margchunne28/Location-data-provider |
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