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 |
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compare size of subpopulations in healthy and AML individuals (within sample analysis) | fig, axarr = plt.subplots(2, 1,sharey=True)
for id in range(0,5):
axarr[0].plot(population_size_H[id],color = 'g')
axarr[0].set_title('healty')
for id in range(0,16):
axarr[1].plot(population_size_SJ[id],color = 'r')
axarr[1].set_title('AML')
plt.show()
X = np.array(population_size_H + population_size_SJ)
Y = ... | _____no_output_____ | MIT | small_run/Flow_Cytometry_Mondrian_Processes-Random-Effects-Final_n_chain_5_n_sample_1000.ipynb | disiji/fc_mondrian |
Diagnosis | # reload data!
data = [data_dict[_].head(20000).applymap(f)[markers].values for _ in ['H1','H2','H3','H4',\
'H5','SJ01','SJ02','SJ03','SJ04','SJ05','SJ06','SJ07','SJ08','SJ09','SJ10',\
'SJ11','SJ12','SJ13','SJ14','SJ15','SJ16']]
# compute data range
data_ranges = np.array([[[data[_][:,d].min... | _____no_output_____ | MIT | small_run/Flow_Cytometry_Mondrian_Processes-Random-Effects-Final_n_chain_5_n_sample_1000.ipynb | disiji/fc_mondrian |
Logistic regression with cell population of under 2 templates as features | # step 1: learn cell populations of all samples, under 2 template MPs, 5 chains
# V: cell proportion for 21 samples under healthy template
V_H = [[None for chain in range(n_mcmc_chain)] for _ in range(21)]
V_SJ = [[None for chain in range(n_mcmc_chain)] for _ in range(21)]
for id in range(21):
print id
res_H ... | [0.99841206336111987, 0.99966800288254687, 0.87316854492456542, 0.99999926620800161, 0.99984613432778913, 1.3459889647293721e-08, 0.0026811637112176268, 0.00010195742044638578, 1.5442242625729463e-05, 1.8254518332594394e-05, 0.003338405513243603, 0.00011531545835186119, 0.00034991109377846552, 0.033424769452122471, 0.0... | MIT | small_run/Flow_Cytometry_Mondrian_Processes-Random-Effects-Final_n_chain_5_n_sample_1000.ipynb | disiji/fc_mondrian |
Baseline 1: one tree for each group (without random effects) | # fit 1 tree to pooled healthy samples
global_MP_H = []
global_MP_SJ = []
n_iter = 1000
data_H = np.concatenate(data[0:5])
for chain in range(n_mcmc_chain):
global_MP_H.append(init_mp(theta_space, table, data_H, n_iter,mcmc_gaussin_std))
data_SJ = np.concatenate(data[5:])
for chain in range(n_mcmc_chain):
... | _____no_output_____ | MIT | small_run/Flow_Cytometry_Mondrian_Processes-Random-Effects-Final_n_chain_5_n_sample_1000.ipynb | disiji/fc_mondrian |
Compare classification error(both gives perfect classification): | V_H_Global = [None for _ in range(21)]
V_SJ_Global = [None for _ in range(21)]
for id in range(21):
V_H_Global[id] = compute_cell_population(data[id], global_MP_H, table, cell_type_name2idx)
V_SJ_Global[id] = compute_cell_population(data[id], global_MP_SJ, table, cell_type_name2idx)
X_Global = [V_H_Global[id... | _____no_output_____ | MIT | small_run/Flow_Cytometry_Mondrian_Processes-Random-Effects-Final_n_chain_5_n_sample_1000.ipynb | disiji/fc_mondrian |
Compare log likelihood $P(data_i|MP_i)$ | # individual MP with random effects
log_lik_H = [[] for _ in range(5)] # 5 * n_chain
log_lik_SJ = [[] for _ in range(16)] # 5 * n_chain
for id in range(5):
data_subset = data[id]
burnt_samples = [i for _ in range(n_mcmc_chain) for i in \
accepts_indiv_mp_lists_H[_][id][-1:]]
for sampl... | _____no_output_____ | MIT | small_run/Flow_Cytometry_Mondrian_Processes-Random-Effects-Final_n_chain_5_n_sample_1000.ipynb | disiji/fc_mondrian |
Baseline 2: K means (use centers of pooled healthy data and pooled AML data as feature extractors) | V_Kmeans_H = [[None for chain in range(n_mcmc_chain)] for _ in range(21)]
V_Kmeans_SJ = [[None for chain in range(n_mcmc_chain)] for _ in range(21)]
from sklearn.cluster import KMeans
from scipy.spatial import distance
for chain in range(n_mcmc_chain):
cluster_centers_H = KMeans(n_clusters=14, random_state=chain)... | _____no_output_____ | MIT | small_run/Flow_Cytometry_Mondrian_Processes-Random-Effects-Final_n_chain_5_n_sample_1000.ipynb | disiji/fc_mondrian |
Random Effect Analysis | def find_first_cut(theta_space):
# find the dimension and location of first cut when there is a cut
root_rec = theta_space[0]
left_rec = theta_space[1][0]
for _ in range(root_rec.shape[0]):
if root_rec[_,1] != left_rec[_,1]:
break
dim, pos = _, left_rec[_,1]... | _____no_output_____ | MIT | small_run/Flow_Cytometry_Mondrian_Processes-Random-Effects-Final_n_chain_5_n_sample_1000.ipynb | disiji/fc_mondrian |
Compare magnitude of random effects in 2 groups | random_effect_H = [[None for chain in range(n_mcmc_chain)] for id in range(5)]
random_effect_SJ = [[None for chain in range(n_mcmc_chain)] for id in range(16)]
for id in range(5):
for chain in range(n_mcmc_chain):
random_effect_H[id][chain] = compute_diff_mp(accepts_template_mp_H[chain][-1],\
... | _____no_output_____ | MIT | small_run/Flow_Cytometry_Mondrian_Processes-Random-Effects-Final_n_chain_5_n_sample_1000.ipynb | disiji/fc_mondrian |
Visualize random effects(find chains and dimensions what random effects are obvious) | chain = 1
random_effect_H_set = [random_effect_H_flattened[id][chain][0] for id in range(5)]
random_effect_SJ_set = [random_effect_SJ_flattened[id][chain][0] for id in range(16)]
# bins = 20
# plt.hist(random_effect_H_set,bins = bins)
# plt.show()
# plt.hist(random_effect_SJ_set, bins = bins)
# plt.show()
# kde_H = Ke... | _____no_output_____ | MIT | small_run/Flow_Cytometry_Mondrian_Processes-Random-Effects-Final_n_chain_5_n_sample_1000.ipynb | disiji/fc_mondrian |
Classifying Surnames with a Multilayer Perceptron Imports | from argparse import Namespace
from collections import Counter
import json
import os
import string
import numpy as np
import pandas as pd
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
from tqdm import tqdm_notebook | _____no_output_____ | Apache-2.0 | chapters/chapter_4/4_2_mlp_surnames/4_2_Classifying_Surnames_with_an_MLP.ipynb | prampampam/PyTorchNLPBook |
Data Vectorization classes The Vocabulary | class Vocabulary(object):
"""Class to process text and extract vocabulary for mapping"""
def __init__(self, token_to_idx=None, add_unk=True, unk_token="<UNK>"):
"""
Args:
token_to_idx (dict): a pre-existing map of tokens to indices
add_unk (bool): a flag that indicates w... | _____no_output_____ | Apache-2.0 | chapters/chapter_4/4_2_mlp_surnames/4_2_Classifying_Surnames_with_an_MLP.ipynb | prampampam/PyTorchNLPBook |
The Vectorizer | class SurnameVectorizer(object):
""" The Vectorizer which coordinates the Vocabularies and puts them to use"""
def __init__(self, surname_vocab, nationality_vocab):
"""
Args:
surname_vocab (Vocabulary): maps characters to integers
nationality_vocab (Vocabulary): maps nati... | _____no_output_____ | Apache-2.0 | chapters/chapter_4/4_2_mlp_surnames/4_2_Classifying_Surnames_with_an_MLP.ipynb | prampampam/PyTorchNLPBook |
The Dataset | class SurnameDataset(Dataset):
def __init__(self, surname_df, vectorizer):
"""
Args:
surname_df (pandas.DataFrame): the dataset
vectorizer (SurnameVectorizer): vectorizer instatiated from dataset
"""
self.surname_df = surname_df
self._vectorizer = vect... | _____no_output_____ | Apache-2.0 | chapters/chapter_4/4_2_mlp_surnames/4_2_Classifying_Surnames_with_an_MLP.ipynb | prampampam/PyTorchNLPBook |
The Model: SurnameClassifier | class SurnameClassifier(nn.Module):
""" A 2-layer Multilayer Perceptron for classifying surnames """
def __init__(self, input_dim, hidden_dim, output_dim):
"""
Args:
input_dim (int): the size of the input vectors
hidden_dim (int): the output size of the first Linear layer... | _____no_output_____ | Apache-2.0 | chapters/chapter_4/4_2_mlp_surnames/4_2_Classifying_Surnames_with_an_MLP.ipynb | prampampam/PyTorchNLPBook |
Training Routine Helper functions | def make_train_state(args):
return {'stop_early': False,
'early_stopping_step': 0,
'early_stopping_best_val': 1e8,
'learning_rate': args.learning_rate,
'epoch_index': 0,
'train_loss': [],
'train_acc': [],
'val_loss': [],
... | _____no_output_____ | Apache-2.0 | chapters/chapter_4/4_2_mlp_surnames/4_2_Classifying_Surnames_with_an_MLP.ipynb | prampampam/PyTorchNLPBook |
general utilities | def set_seed_everywhere(seed, cuda):
np.random.seed(seed)
torch.manual_seed(seed)
if cuda:
torch.cuda.manual_seed_all(seed)
def handle_dirs(dirpath):
if not os.path.exists(dirpath):
os.makedirs(dirpath) | _____no_output_____ | Apache-2.0 | chapters/chapter_4/4_2_mlp_surnames/4_2_Classifying_Surnames_with_an_MLP.ipynb | prampampam/PyTorchNLPBook |
Settings and some prep work | args = Namespace(
# Data and path information
surname_csv="data/surnames/surnames_with_splits.csv",
vectorizer_file="vectorizer.json",
model_state_file="model.pth",
save_dir="model_storage/ch4/surname_mlp",
# Model hyper parameters
hidden_dim=300,
# Training hyper parameters
seed=13... | Expanded filepaths:
model_storage/ch4/surname_mlp/vectorizer.json
model_storage/ch4/surname_mlp/model.pth
Using CUDA: False
| Apache-2.0 | chapters/chapter_4/4_2_mlp_surnames/4_2_Classifying_Surnames_with_an_MLP.ipynb | prampampam/PyTorchNLPBook |
Initializations | if args.reload_from_files:
# training from a checkpoint
print("Reloading!")
dataset = SurnameDataset.load_dataset_and_load_vectorizer(args.surname_csv,
args.vectorizer_file)
else:
# create dataset and vectorizer
print("Creating fresh!")
... | Creating fresh!
| Apache-2.0 | chapters/chapter_4/4_2_mlp_surnames/4_2_Classifying_Surnames_with_an_MLP.ipynb | prampampam/PyTorchNLPBook |
Training loop | classifier = classifier.to(args.device)
dataset.class_weights = dataset.class_weights.to(args.device)
loss_func = nn.CrossEntropyLoss(dataset.class_weights)
optimizer = optim.Adam(classifier.parameters(), lr=args.learning_rate)
scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer=optimizer,
... | Test loss: 1.7435305690765381;
Test Accuracy: 47.875
| Apache-2.0 | chapters/chapter_4/4_2_mlp_surnames/4_2_Classifying_Surnames_with_an_MLP.ipynb | prampampam/PyTorchNLPBook |
Inference | def predict_nationality(surname, classifier, vectorizer):
"""Predict the nationality from a new surname
Args:
surname (str): the surname to classifier
classifier (SurnameClassifer): an instance of the classifier
vectorizer (SurnameVectorizer): the corresponding vectorizer
Return... | Enter a surname to classify: McMahan
McMahan -> Irish (p=0.55)
| Apache-2.0 | chapters/chapter_4/4_2_mlp_surnames/4_2_Classifying_Surnames_with_an_MLP.ipynb | prampampam/PyTorchNLPBook |
Top-K Inference | vectorizer.nationality_vocab.lookup_index(8)
def predict_topk_nationality(name, classifier, vectorizer, k=5):
vectorized_name = vectorizer.vectorize(name)
vectorized_name = torch.tensor(vectorized_name).view(1, -1)
prediction_vector = classifier(vectorized_name, apply_softmax=True)
probability_values, i... | Enter a surname to classify: McMahan
How many of the top predictions to see? 5
Top 5 predictions:
===================
McMahan -> Irish (p=0.55)
McMahan -> Scottish (p=0.21)
McMahan -> Czech (p=0.05)
McMahan -> German (p=0.04)
McMahan -> English (p=0.03)
| Apache-2.0 | chapters/chapter_4/4_2_mlp_surnames/4_2_Classifying_Surnames_with_an_MLP.ipynb | prampampam/PyTorchNLPBook |
Task 2 dpm.load_task2() | dpm.load_task2()
data = dpm.train_task2_df
data["keyword"].value_counts().keys()
data["text"][0]
def get_text_for(data,label = 0):
"""
Returns text that corresponds to the label as a single string.
"""
text = []
for i in range(len(data["text"])):
if data["label"][i][label] == 1:
... | zip warning: name not matched: task2.txt
updating: task1.txt (deflated 92%)
| MIT | proto_two.ipynb | sarveshbhatnagar/PCL_DETECTION |
Pre-processing mouse dataLoad the clusters obtained from previous section, so that we can bootsrap on them. | ### Load DataFrame of log-transfromed averaged time-series for each cluster
# (1) Healthy Group
df_log_healthy = pd.read_pickle("data/df_log_healthy.pkl")
# (2) IBD Group
df_log_ibd = pd.read_pickle("data/df_log_ibd.pkl")
### Load cluster memberships for every OTU
# (1) Healthy Group
tree_healthy = pd.read_pickle( "da... | _____no_output_____ | MIT | Bootstrapping_augmentation.ipynb | sytseng/CS109b_Final_Project_Spring_2019 |
Bootstrapping Step A. Subset df by cluster MembershipRecall that we have three methods to generate clusters: - Tree based: 3 clusters- NMF correlation: 9 clusters- Time correlation: 5 clustersAnd we have loaded the cluster membership for every OTU above. In this section, we will subset the OTU into those different clu... | ### Function to subset the dataframe by cluster membership
def subset_df_by_membership(df, tree, NMF, time):
# get the total number of otu and time points
(otu_length,time_length) = df.shape
# add the membership as the last column
df['tree']=tree
df['NMF']=NMF
df['time']=time
# loop th... | _____no_output_____ | MIT | Bootstrapping_augmentation.ipynb | sytseng/CS109b_Final_Project_Spring_2019 |
Step B. Bootstrap to generate more mice dataNow that we have the clusters, we do bootstrap:- For each single sample step, within every cluster, we randomly choose 30% of the OTUs, took the average of them to generate one time series representing that cluster.- We repeated the sampling for 30 times, to generate the 30 ... | ### Function to Bootstrap:
def bootrapping(method_list, mice_count):
methods = list()
for method in range(3):
mice = list()
for time in range(mice_count):
clusters = list()
for cluster in range(len(method_list[method])):
one_sample = method_list[method][cl... | _____no_output_____ | MIT | Bootstrapping_augmentation.ipynb | sytseng/CS109b_Final_Project_Spring_2019 |
Data Structure ExampleThese are the simulated absolute values (not the log-transformed, they have already been transformed back). | #######################################################################
# For example: tree_healthy_30_mice #
# tree_healthy_30_mice: the first mice data #
# tree_healthy_30_mice[0]: the first cluster in the first mice data #
# tree_healthy_30_mice[0][0]: th... | the nunmber of simulated mice data is: 30
within each mouse, the number of the tree_based clusters is: 3
for each cluster, the number of the time points is: 75
| MIT | Bootstrapping_augmentation.ipynb | sytseng/CS109b_Final_Project_Spring_2019 |
Fashion MNIST | from keras.datasets import fashion_mnist
from sklearn.metrics import roc_auc_score
from sklearn.metrics import mean_squared_error
_, (fashion_x_test, _) = fashion_mnist.load_data()
fashion_x_test = fashion_x_test.astype('float32') / 255.
fashion_x_test = np.reshape(fashion_x_test, (len(x_test), 28, 28, 1))
show_10_i... | AUROC: 0.99937089
| MIT | notebooks/autoencoders/MNIST10/one_anomaly_detector.ipynb | tayden/NoveltyDetection |
EMNIST Letters | from torchvision.datasets import EMNIST
emnist_letters = EMNIST('./', "letters", train=False, download=True)
emnist_letters = emnist_letters.test_data.numpy()
emnist_letters = emnist_letters.astype('float32') / 255.
emnist_letters = np.swapaxes(emnist_letters, 1, 2)
emnist_letters = np.reshape(emnist_letters, (len(em... | AUROC: 0.9604927475961538
| MIT | notebooks/autoencoders/MNIST10/one_anomaly_detector.ipynb | tayden/NoveltyDetection |
Gaussian Noise | mnist_mean = np.mean(x_train)
mnist_std = np.std(x_train)
gaussian_data = np.random.normal(mnist_mean, mnist_std, size=(10000, 28, 28, 1))
show_10_images(gaussian_data)
show_10_images(autoencoder.predict(gaussian_data))
labels = len(x_test) * [0] + len(gaussian_data) * [1]
test_samples = np.concatenate((x_test, gaussi... | AUROC: 1.0
| MIT | notebooks/autoencoders/MNIST10/one_anomaly_detector.ipynb | tayden/NoveltyDetection |
Uniform Noise | import math
b = math.sqrt(3.) * mnist_std
a = -b + mnist_mean
b += mnist_mean
uniform_data = np.random.uniform(low=a, high=b, size=(10000, 28, 28, 1))
show_10_images(uniform_data)
show_10_images(autoencoder.predict(uniform_data))
labels = len(x_test) * [0] + len(uniform_data) * [1]
test_samples = np.concatenate((x_te... | AUROC: 1.0
| MIT | notebooks/autoencoders/MNIST10/one_anomaly_detector.ipynb | tayden/NoveltyDetection |
Check River Flows | from __future__ import division
import matplotlib.pyplot as plt
import netCDF4 as nc
import numpy as np
from salishsea_tools import nc_tools
%matplotlib inline
def find_points(flow):
for i in range(390,435):
for j in range(280,398):
if flow1[0,i,j] > 0:
print i,j, lat[i,j], lo... | _____no_output_____ | Apache-2.0 | Susan/Check River Files.ipynb | SalishSeaCast/analysis |
import pandas as pd
ws = pd.read_csv('winshares.txt')
ws.head()
ws.shape
# clean up player name column
ws['Player'] = ws['Player'].str.split('/').str[0]
ws.head()
sals = pd.read_csv('salaries.txt')
sals.head()
sals.shape
sals['Player'] = sals['Player'].str.split('/').str[0]
sals.head()
# merge columns 2019-2020 salari... | _____no_output_____ | MIT | dataproject.ipynb | mugilc/mugilc.github.io | |
- How many items are NaN in the is hk column?- How many items are known housekeeping genes?- How many items are known tissue specific genes? | print("NaN %s" % len(data[data["is_hk"].isnull()]))
print("Housekeeping %s" % len(data[data["is_hk"] == 1]))
print("Specific %s" % len(data[data["is_hk"] == 0]))
def split_train_test(data):
split = (int) (len(data) * 0.9)
return data[0:split], data[split:]
def split_data(data):
# Shuffle data
data = da... | _____no_output_____ | MIT | gene-prediction-gaussian.ipynb | neungkl/MLE-and-naive-bayes-classification |
How many bins have zero counts? | print("Total %s" % len(hist))
print("Zeros %s" % sum(hist == 0)) | Total 1000
Zeros 823
| MIT | gene-prediction-gaussian.ipynb | neungkl/MLE-and-naive-bayes-classification |
**cDNA Density Plot** | train_set_clength_no_nan_sorted = data["cDNA_length"][data["cDNA_length"].notnull()].sort_values()
bin_edge = np.unique(train_set_clength_no_nan_sorted[0::70])
hist = np.bincount(np.digitize(train_set_clength_no_nan_sorted, bin_edge))
hist = hist[1:-1]
bin_plot(hist, bin_edge) | _____no_output_____ | MIT | gene-prediction-gaussian.ipynb | neungkl/MLE-and-naive-bayes-classification |
**CDS Density Plot** | train_set_clength_no_nan_sorted = data["cds_length"][data["cds_length"].notnull()].sort_values()
bin_edge = np.unique(train_set_clength_no_nan_sorted[0::100])
hist = np.bincount(np.digitize(train_set_clength_no_nan_sorted, bin_edge))
hist = hist[1:-1]
bin_plot(hist, bin_edge)
for feature in list(train_set):
if fea... | _____no_output_____ | MIT | gene-prediction-gaussian.ipynb | neungkl/MLE-and-naive-bayes-classification |
MLE calculation | def calc_mean_var(data):
data = data[data.notnull()]
u = data.mean()
v = data.var()
return u, v
def calc_prob_eq_zero(data):
data = data[data.notnull()]
return len(data[data == 0]) * 1.0 / len(data)
likelihood = {}
for feature in list(train_set):
if feature == "is_hk":
continue
... | Accuracy: 0.923077
Precision: 0.647059
Recall: 1.000000
F1: 0.785714
| MIT | gene-prediction-gaussian.ipynb | neungkl/MLE-and-naive-bayes-classification |
Baseline 1\. Random Choice Baseline | def create_random_pred():
return np.random.random_sample((len(y_test),)) - 0.5
y_pred = activate_predict(create_random_pred())
measure_metrics(y_test, y_pred) | Accuracy: 0.435897
Precision: 0.133333
Recall: 0.545455
F1: 0.214286
| MIT | gene-prediction-gaussian.ipynb | neungkl/MLE-and-naive-bayes-classification |
2\. Majority | def create_majority_pred():
return np.ones(len(y_test)) * test_set["is_hk"].mode().values.astype(int)
y_pred = create_majority_pred()
measure_metrics(y_test, y_pred) | Accuracy: 0.858974
Precision: 0.000000
Recall: 0.000000
F1: 0.000000
| MIT | gene-prediction-gaussian.ipynb | neungkl/MLE-and-naive-bayes-classification |
ROC | t = np.arange(-5,5,0.01)
tp = []
tp_random = []
tp_majority = []
fp = []
fp_random = []
fp_majority = []
y_test = test_set["is_hk"]
y_pred = predict(test_set)
y_random = create_random_pred()
y_act_majority = create_majority_pred()
for t_i in t:
y_act_pred = activate_predict(y_pred, threshold = t_i)
y_... | _____no_output_____ | MIT | gene-prediction-gaussian.ipynb | neungkl/MLE-and-naive-bayes-classification |
Getting started with TensorFlow (Graph Mode)**Learning Objectives** - Understand the difference between Tensorflow's two modes: Eager Execution and Graph Execution - Get used to deferred execution paradigm: first define a graph then run it in a `tf.Session()` - Understand how to parameterize a graph using `tf.place... | import tensorflow as tf
print(tf.__version__) | _____no_output_____ | Apache-2.0 | courses/machine_learning/deepdive/02_tensorflow/b_tfstart_graph.ipynb | KayvanShah1/training-data-analyst |
Graph Execution Adding Two Tensors Build the GraphUnlike eager mode, no concrete value will be returned yet. Just a name, shape and type are printed. Behind the scenes a directed graph is being created. | a = tf.constant(value = [5, 3, 8], dtype = tf.int32)
b = tf.constant(value = [3, -1, 2], dtype = tf.int32)
c = tf.add(x = a, y = b)
print(c) | _____no_output_____ | Apache-2.0 | courses/machine_learning/deepdive/02_tensorflow/b_tfstart_graph.ipynb | KayvanShah1/training-data-analyst |
Run the GraphA graph can be executed in the context of a `tf.Session()`. Think of a session as the bridge between the front-end Python API and the back-end C++ execution engine. Within a session, passing a tensor operation to `run()` will cause Tensorflow to execute all upstream operations in the graph required to cal... | with tf.Session() as sess:
result = sess.run(fetches = c)
print(result) | _____no_output_____ | Apache-2.0 | courses/machine_learning/deepdive/02_tensorflow/b_tfstart_graph.ipynb | KayvanShah1/training-data-analyst |
Parameterizing the Grpah What if values of `a` and `b` keep changing? How can you parameterize them so they can be fed in at runtime? *Step 1: Define Placeholders*Define `a` and `b` using `tf.placeholder()`. You'll need to specify the data type of the placeholder, and optionally a tensor shape.*Step 2: Provide feed_di... | a = tf.placeholder(dtype = tf.int32, shape = [None])
b = tf.placeholder(dtype = tf.int32, shape = [None])
c = tf.add(x = a, y = b)
with tf.Session() as sess:
result = sess.run(fetches = c, feed_dict = {
a: [3, 4, 5],
b: [-1, 2, 3]
})
print(result) | _____no_output_____ | Apache-2.0 | courses/machine_learning/deepdive/02_tensorflow/b_tfstart_graph.ipynb | KayvanShah1/training-data-analyst |
Linear Regression Toy DatasetWe'll model the following:\begin{equation}y= 2x + 10\end{equation} | X = tf.constant(value = [1,2,3,4,5,6,7,8,9,10], dtype = tf.float32)
Y = 2 * X + 10
print("X:{}".format(X))
print("Y:{}".format(Y)) | _____no_output_____ | Apache-2.0 | courses/machine_learning/deepdive/02_tensorflow/b_tfstart_graph.ipynb | KayvanShah1/training-data-analyst |
2.2 Loss FunctionUsing mean squared error, our loss function is:\begin{equation}MSE = \frac{1}{m}\sum_{i=1}^{m}(\hat{Y}_i-Y_i)^2\end{equation}$\hat{Y}$ represents the vector containing our model's predictions:\begin{equation}\hat{Y} = w_0X + w_1\end{equation}Note below we introduce TF variables for the first time. Unl... | with tf.variable_scope(name_or_scope = "training", reuse = tf.AUTO_REUSE):
w0 = tf.get_variable(name = "w0", initializer = tf.constant(value = 0.0, dtype = tf.float32))
w1 = tf.get_variable(name = "w1", initializer = tf.constant(value = 0.0, dtype = tf.float32))
Y_hat = w0 * X + w1
loss_mse = tf.reduce_mea... | _____no_output_____ | Apache-2.0 | courses/machine_learning/deepdive/02_tensorflow/b_tfstart_graph.ipynb | KayvanShah1/training-data-analyst |
OptimizerAn optimizer in TensorFlow both calculates gradients and updates weights. In addition to basic gradient descent, TF provides implementations of several more advanced optimizers such as ADAM and FTRL. They can all be found in the [tf.train](https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/train) m... | LEARNING_RATE = tf.placeholder(dtype = tf.float32, shape = None)
optimizer = tf.train.GradientDescentOptimizer(learning_rate = LEARNING_RATE).minimize(loss = loss_mse) | _____no_output_____ | Apache-2.0 | courses/machine_learning/deepdive/02_tensorflow/b_tfstart_graph.ipynb | KayvanShah1/training-data-analyst |
Training LoopNote our results are identical to what we found in Eager mode. | STEPS = 1000
with tf.Session() as sess:
sess.run(tf.global_variables_initializer()) # initialize variables
for step in range(STEPS):
#1. Calculate gradients and update seights
sess.run(fetches = optimizer, feed_dict = {LEARNING_RATE: 0.02})
#2. Periodically print MSE
... | _____no_output_____ | Apache-2.0 | courses/machine_learning/deepdive/02_tensorflow/b_tfstart_graph.ipynb | KayvanShah1/training-data-analyst |
This is only the tested and reported cases John Hopkins CCSE has data for this is by no means a definitive view of the global epidemic. The repo is updated daily around 5:00pm PDT | import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
confirmed_url = "https://github.com/CSSEGISandData/COVID-19/raw/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv"
recovered_url = "https://github.com/CSSEGISandData/COVID-19/raw/mast... | _____no_output_____ | MIT | Covid19.ipynb | BryanSouza91/COVID-19 |
Plots total confirmed cases by country Changing the logx=False to True shows the logarithmic scales of x-axis Changing the logy=False to True shows the logarithmic scales of y-axis Changing the loglog=False to True shows the logarithmic scales of both axes | conf_df.loc[:,'1/22/20':].loc[conf_df['Country/Region'] == 'China'].sum().plot(figsize=(25,6),logx=False,logy=False,loglog=True);
conf_df.loc[:,'1/22/20':].loc[conf_df['Country/Region'] == 'US'].sum().plot(figsize=(25,6),logx=False,logy=False,loglog=False);
conf_df.loc[:,'1/22/20':].loc[conf_df['Country/Region'] == 'Ja... | _____no_output_____ | MIT | Covid19.ipynb | BryanSouza91/COVID-19 |
World report |
# Create reusable series objects
conf_sum = conf_df.loc[:,'1/22/20':].sum()
recv_sum = recv_df.loc[:,'1/22/20':].sum()
death_sum = death_df.loc[:,'1/22/20':].sum()
conf_sum_dif = difference(conf_sum, 1).values
recv_sum_dif = difference(recv_sum, 1).values
death_sum_dif = difference(death_sum, 1).values
# Print world... | World numbers current as of 4/5/20
New cases: 74710
Total confirmed cases: 1272115
New case rate: 6.239%
New case 7-day Moving Average: 78857
New case 30-day Moving Average: 39010
New Recovered cases: 13860
Total recover... | MIT | Covid19.ipynb | BryanSouza91/COVID-19 |
Report for each country reporting cases | # define report function
def report(country):
# Create reusable series objects
country_conf_sum = conf_df.loc[:,'1/22/20':].loc[conf_df['Country/Region'] == country].sum()
country_recv_sum = recv_df.loc[:,'1/22/20':].loc[conf_df['Country/Region'] == country].sum()
country_death_sum = death_df.loc[:,'1/... | _____no_output_____ | MIT | Covid19.ipynb | BryanSouza91/COVID-19 |
!git clone https://github.com/AadSah/fixmatch.git
!scp -r ./fixmatch/* ./
!pip install -r ./requirements.txt
!mkdir datasets
!pip uninstall tensorflow
!pip uninstall tensorflow-gpu
!pip install tensorflow-gpu==1.14.0
!pip install libml
import os
os.environ['ML_DATA'] = './datasets'
%set_env PYTHONPATH=$PYTHONPATH:.
!CU... | _____no_output_____ | Apache-2.0 | covid_fixmatch_xray.ipynb | AadSah/fixmatch | |
Get NFL Player Data
| import requests
from pandas.io.json import json_normalize
import pandas as pd
import requests
# https://sportsdata.io/developers/api-documentation/nfl
# Player overall information
#url = "https://api.sportsdata.io/v3/nfl/scores/json/Players?key=d072122708d34423857116889b72f55b"
# Player Season stats for 20... | _____no_output_____ | Apache-2.0 | NFL (1).ipynb | humberhutch/NFLAnalysis |
Show the first few rows of data returned - All players | df.head() | _____no_output_____ | Apache-2.0 | NFL (1).ipynb | humberhutch/NFLAnalysis |
Focus on Wide Receivers | wr = df[ df['Position'] =='WR' ]
print (wr.shape) # Number of players (rows) and attributes (columns)
# remove players with few games played or less than 10 Receiving Yards
wr = wr[ wr['Played'] >10]
wr = wr[ wr['ReceivingYards'] >10]
wr.describe()
yardsPerGame = wr['ReceivingYards']/wr['Played']
wr['yardsPerG... | _____no_output_____ | Apache-2.0 | NFL (1).ipynb | humberhutch/NFLAnalysis |
Create a histogram of the Yards Per Game | wr['yardsPerGame'].hist()
| _____no_output_____ | Apache-2.0 | NFL (1).ipynb | humberhutch/NFLAnalysis |
Boxplot to show distribution | wr['yardsPerGame'].plot.box(); | _____no_output_____ | Apache-2.0 | NFL (1).ipynb | humberhutch/NFLAnalysis |
Keep the main columns for analysis | colsKeep = ['PlayerID', 'Season','Team', 'Activated','Played','Started','ReceivingTargets', 'Receptions', 'ReceivingYards', 'ReceivingYardsPerReception','ReceivingTouchdowns','ReceivingLong','yardsPerGame']
new_wr = wr[colsKeep]
new_wr.head() | _____no_output_____ | Apache-2.0 | NFL (1).ipynb | humberhutch/NFLAnalysis |
Retrieve data for all players for past 3 years and add salary for analysis
| new_wr.groupby(['Team']).mean()['yardsPerGame']
| _____no_output_____ | Apache-2.0 | NFL (1).ipynb | humberhutch/NFLAnalysis |
Dummy Variables ExerciseIn this exercise, you'll create dummy variables from the projects data set. The idea is to transform categorical data like this:| Project ID | Project Category ||------------|------------------|| 0 | Energy || 1 | Transportation || 2 | Health || ... | import pandas as pd
import numpy as np
# read in the projects data set and do basic wrangling
projects = pd.read_csv('../data/projects_data.csv', dtype=str)
projects.drop('Unnamed: 56', axis=1, inplace=True)
projects['totalamt'] = pd.to_numeric(projects['totalamt'].str.replace(',', ''))
projects['countryname'] = proj... | _____no_output_____ | MIT | lessons/ETLPipelines/12_dummyvariables_exercise/12_dummyvariables_exercise.ipynb | rabadzhiyski/Data_Science_Udacity |
Run the code cell below. This cell shows the percentage of each variable that is null. Notice the mjsector1 through mjsector5 variables are all null. The mjtheme1name through mjtheme5name are also all null as well as the theme variable. Because these variables contain so many null values, they're probably not very usef... | # output percentage of values that are missing
100 * sector.isnull().sum() / sector.shape[0] | _____no_output_____ | MIT | lessons/ETLPipelines/12_dummyvariables_exercise/12_dummyvariables_exercise.ipynb | rabadzhiyski/Data_Science_Udacity |
The sector1 variable looks promising; it doesn't contain any null values at all. In the next cell, store the unique sector1 values in a list and output the results. Use the sort_values() and unique() methods. | # TODO: Create a list of the unique values in sector1. Use the sort_values() and unique() pandas methods.
# And then convert those results into a Python list
uniquesectors1 = list(sector['sector1'].sort_values().unique())
uniquesectors1
# run this code cell to see the number of unique values
print('Number of unique va... | Number of unique values in sector1: 3060
| MIT | lessons/ETLPipelines/12_dummyvariables_exercise/12_dummyvariables_exercise.ipynb | rabadzhiyski/Data_Science_Udacity |
3060 different categories is quite a lot! Remember that with dummy variables, if you have n categorical values, you need n - 1 new variables! That means 3059 extra columns! Exercise 2There are a few issues with this 'sector1' variable. First, there are values labeled '!$!0'. These should be substituted with NaN.Furthe... | # TODO: In the sector1 variable, replace the string '!$10' with nan
# HINT: you can use the pandas replace() method and numpy.nan
sector['sector1'] = sector['sector1'].replace('!$!0', np.nan)
# TODO: In the sector1 variable, remove the last 10 or 11 characters from the sector1 variable.
# HINT: There is more than one ... | Number of unique sectors after cleaning: 156
Percentage of null values after cleaning: 3.4962735642262164
| MIT | lessons/ETLPipelines/12_dummyvariables_exercise/12_dummyvariables_exercise.ipynb | rabadzhiyski/Data_Science_Udacity |
Now there are 156 unique categorical values. That's better than 3060. If you were going to use this data with a supervised learning machine model, you could try converting these 156 values to dummy variables. You'd still have to train and test a model to see if those are good features.But can you do anything else with ... | sector['sector'] | _____no_output_____ | MIT | lessons/ETLPipelines/12_dummyvariables_exercise/12_dummyvariables_exercise.ipynb | rabadzhiyski/Data_Science_Udacity |
What else can you do? If you look at all of the diferent sector1 categories, it might be useful to combine a few of them together. For example, there are various categories with the term "Energy" in them. And then there are other categories that seem related to energy but don't have the word energy in them like "Therma... | import re
# Create the sector1_aggregates variable
sector.loc[:,'sector1_aggregates'] = sector['sector1']
# TODO: The code above created a new variable called sector1_aggregates.
# Currently, sector1_aggregates has all of the same values as sector1
# For this task, find all the rows in sector1_aggregates... | Number of unique sectors after cleaning: 145
| MIT | lessons/ETLPipelines/12_dummyvariables_exercise/12_dummyvariables_exercise.ipynb | rabadzhiyski/Data_Science_Udacity |
The number of unique sectors continues to go down. Keep in mind that how much to consolidate will depend on your machine learning model performance and your hardware's ability to handle the extra features in memory. If your hardware's memory can handle 3060 new features and your machine learning algorithm performs bett... | # TODO: Create dummy variables from the sector1_aggregates data. Put the results into a dataframe called dummies
# Hint: Use the get_dummies method
dummies = pd.DataFrame(pd.get_dummies(sector['sector1_aggregates']))
# TODO: Filter the projects data for the totalamt, the year from boardapprovaldate, and the dummy vari... | _____no_output_____ | MIT | lessons/ETLPipelines/12_dummyvariables_exercise/12_dummyvariables_exercise.ipynb | rabadzhiyski/Data_Science_Udacity |
load data | DATASET_ID = 'BIRD_DB_Vireo_cassinii'
df_loc = DATA_DIR / 'syllable_dfs' / DATASET_ID / 'cassins.pickle'
syllable_df = pd.read_pickle(df_loc)
del syllable_df['audio']
syllable_df[:3]
np.shape(syllable_df.spectrogram.values[0]) | _____no_output_____ | MIT | notebooks/02.5-make-projection-dfs/higher-spread/.ipynb_checkpoints/cassins-umap-checkpoint.ipynb | xingjeffrey/avgn_paper |
project | specs = list(syllable_df.spectrogram.values)
specs = [i/np.max(i) for i in tqdm(specs)]
specs_flattened = flatten_spectrograms(specs)
np.shape(specs_flattened)
cuml_umap = cumlUMAP(min_dist = 0.5)
embedding = cuml_umap.fit_transform(specs_flattened)
fig, ax = plt.subplots()
ax.scatter(embedding[:,0], embedding[:,1], s=... | _____no_output_____ | MIT | notebooks/02.5-make-projection-dfs/higher-spread/.ipynb_checkpoints/cassins-umap-checkpoint.ipynb | xingjeffrey/avgn_paper |
Save | ensure_dir(DATA_DIR / 'embeddings' / DATASET_ID / 'full')
syllable_df.to_pickle(DATA_DIR / 'embeddings' / DATASET_ID / (str(min_dist) + '_full.pickle')) | _____no_output_____ | MIT | notebooks/02.5-make-projection-dfs/higher-spread/.ipynb_checkpoints/cassins-umap-checkpoint.ipynb | xingjeffrey/avgn_paper |
Qualitatively replicate: [1] Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14(2), 179–211. https://doi.org/10.1016/0364-0213(90)90002-E[2] Saffran, J. R., Aslin, R. N., & Newport, E. L. (1996). Statistical learning by 8-month-old infants. Science, 274(5294), 1926–1928. https://doi.org/10.1126/scien... | import os
import time
import warnings
import itertools
import numpy as np
from sklearn.preprocessing import OneHotEncoder
import torch
import torch.nn as nn
import matplotlib.pyplot as plt
import seaborn as sns
warnings.filterwarnings("ignore")
sns.set(style='white', context='poster', font_scale=.8, rc={"lines.lin... | _____no_output_____ | MIT | elman_pytorch.ipynb | qihongl/demo-elman-1990 |
Pandas II More indexing tricks We'll start out with some data from Beer Advocate (see [Tom Augspurger](https://github.com/TomAugspurger/pydata-chi-h2t/blob/master/3-Indexing.ipynb) for some cool details on how he extracted this data) | import numpy as np
import pandas as pd
pd.options.display.max_rows = 10
df = pd.read_csv('data/beer_subset.csv.gz', parse_dates=['time'], compression='gzip') | _____no_output_____ | CC-BY-3.0 | Lecture 4/Lecture 4 - Pandas II (Template).ipynb | iEvidently/ihme-python-course |
Boolean indexingLike a where clause in SQL. The indexer (or boolean mask) should be 1-dimensional and the same length as the thing being indexed. | df.loc[((df['abv'] < 5) & (df['time'] > pd.Timestamp('2009-06'))) |
(df['review_overall'] >= 4.5)].head() | _____no_output_____ | CC-BY-3.0 | Lecture 4/Lecture 4 - Pandas II (Template).ipynb | iEvidently/ihme-python-course |
Be careful with the order of operations... Safest to use parentheses... Select just the rows where the `beer_style` contains `'IPA'`: Find the rows where the beer style is either `'American IPA'` or `'Pilsner'`: | (df['beer_style'] == 'American IPA') | _____no_output_____ | CC-BY-3.0 | Lecture 4/Lecture 4 - Pandas II (Template).ipynb | iEvidently/ihme-python-course |
Or more succinctly: | df[df['beer_style'].isin(['American IPA', 'Pilsner'])].head() | _____no_output_____ | CC-BY-3.0 | Lecture 4/Lecture 4 - Pandas II (Template).ipynb | iEvidently/ihme-python-course |
Mini Exercise- Select the rows where the scores of the 5 review_cols ('review_appearance', 'review_aroma', 'review_overall', 'review_palate', 'review_taste') are all at least 4.0.- _Hint_: Like NumPy arrays, DataFrames have an any and all methods that check whether it contains any or all True values. These methods als... | reviews = df.set_index(['profile_name', 'beer_id', 'time']) | _____no_output_____ | CC-BY-3.0 | Lecture 4/Lecture 4 - Pandas II (Template).ipynb | iEvidently/ihme-python-course |
Top ReviewersLet's select all the reviews by the top reviewers, by label. The syntax is a bit trickier when you want to specify a row Indexer *and* a column Indexer: | reviews.loc[(top_reviewers, 99, :), ['beer_name', 'brewer_name']]
reviews.loc[pd.IndexSlice[top_reviewers, 99, :], ['beer_name', 'brewer_id']] | _____no_output_____ | CC-BY-3.0 | Lecture 4/Lecture 4 - Pandas II (Template).ipynb | iEvidently/ihme-python-course |
Use `.loc` to select the `beer_name` and `beer_style` for the 10 most popular beers, as measured by number of reviews: Beware "chained indexing"You can sometimes get away with using `[...][...]`, but try to avoid it! | df.loc[df['beer_style'].str.contains('IPA')]['beer_name']
df.loc[df['beer_style'].str.contains('IPA')]['beer_name'] = 'yummy'
df.loc[df['beer_style'].str.contains('IPA')]['beer_name'] | _____no_output_____ | CC-BY-3.0 | Lecture 4/Lecture 4 - Pandas II (Template).ipynb | iEvidently/ihme-python-course |
Dates and Times - Date and time data are inherently problematic - An unequal number of days in every month - An unequal number of days in a year (due to leap years) - Time zones that vary over space - etc - The datetime built-in library handles temporal information down to the nanosecond Having a custom... | segments = pd.read_csv('data/AIS/transit_segments.csv') | _____no_output_____ | CC-BY-3.0 | Lecture 4/Lecture 4 - Pandas II (Template).ipynb | iEvidently/ihme-python-course |
For example, we might be interested in the distribution of transit lengths, so we can plot them as a histogram: Though most of the transits appear to be short, there are a few longer distances that make the plot difficult to read. This is where a transformation is useful: We can see that although there are date/time fi... | datetime.strptime(segments['st_time'].ix[0], '%m/%d/%y %H:%M') | _____no_output_____ | CC-BY-3.0 | Lecture 4/Lecture 4 - Pandas II (Template).ipynb | iEvidently/ihme-python-course |
As a convenience, Pandas has a `to_datetime` method that will parse and convert an entire Series of formatted strings into `datetime` objects. Pandas also has a custom NA value for missing datetime objects, `NaT`. | pd.to_datetime([None]) | _____no_output_____ | CC-BY-3.0 | Lecture 4/Lecture 4 - Pandas II (Template).ipynb | iEvidently/ihme-python-course |
Finally, if `to_datetime()` has problems parsing any particular date/time format, you can pass the spec in using the `format=` argument. Merging and joining `DataFrame`s In Pandas, we can combine tables according to the value of one or more *keys* that are used to identify rows, much like an index. | df1 = pd.DataFrame({'id': range(4),
'age': np.random.randint(18, 31, size=4)})
df2 = pd.DataFrame({'id': list(range(3))*2,
'score': np.random.random(size=6)}) | _____no_output_____ | CC-BY-3.0 | Lecture 4/Lecture 4 - Pandas II (Template).ipynb | iEvidently/ihme-python-course |
Notice that without any information about which column to use as a key, Pandas did the right thing and used the `id` column in both tables. Unless specified otherwise, `merge` will used any common column names as keys for merging the tables. Notice also that `id=3` from `df1` was omitted from the merged table. This is... | vessels = pd.read_csv('data/AIS/vessel_information.csv', index_col='mmsi') | _____no_output_____ | CC-BY-3.0 | Lecture 4/Lecture 4 - Pandas II (Template).ipynb | iEvidently/ihme-python-course |
We see that there is a `mmsi` value (a vessel identifier) in each table, but it is used as an index for the `vessels` table. In this case, we have to specify to join on the index for this table, and on the `mmsi` column for the other. Notice that `mmsi` field that was an index on the `vessels` table is no longer an ind... | cdystonia = pd.read_csv('data/cdystonia.csv', index_col=None) | _____no_output_____ | CC-BY-3.0 | Lecture 4/Lecture 4 - Pandas II (Template).ipynb | iEvidently/ihme-python-course |
This dataset includes repeated measurements of the same individuals (longitudinal data). Its possible to present such information in (at least) two ways: showing each repeated measurement in their own row, or in multiple columns representing multiple measurements. `.stack()` rotates the data frame so that columns are r... | cdystonia2 = cdystonia.set_index(['patient','obs']) | _____no_output_____ | CC-BY-3.0 | Lecture 4/Lecture 4 - Pandas II (Template).ipynb | iEvidently/ihme-python-course |
If we want to transform this data so that repeated measurements are in columns, we can `unstack` the `twstrs` measurements according to `obs`: And if we want to keep the other variables: | cdystonia_wide = (cdystonia[['patient','site','id','treat','age','sex']]
.drop_duplicates()
.merge(twstrs_wide, right_index=True, left_on='patient', how='inner')
.head()) | _____no_output_____ | CC-BY-3.0 | Lecture 4/Lecture 4 - Pandas II (Template).ipynb | iEvidently/ihme-python-course |
Or to simplify things, we can set the patient-level information as an index before unstacking: | (cdystonia.set_index(['patient','site','id','treat','age','sex','week'])['twstrs']
.unstack('week').head()) | _____no_output_____ | CC-BY-3.0 | Lecture 4/Lecture 4 - Pandas II (Template).ipynb | iEvidently/ihme-python-course |
[`.melt()`](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.melt.html)- To convert our "wide" format back to long, we can use the `melt` function. - This function is useful for `DataFrame`s where one or more columns are identifier variables (`id_vars`), with the remaining columns being measured variables ... | pd.melt(cdystonia_wide, id_vars=['patient','site','id','treat','age','sex'],
var_name='obs', value_name='twsters').head() | _____no_output_____ | CC-BY-3.0 | Lecture 4/Lecture 4 - Pandas II (Template).ipynb | iEvidently/ihme-python-course |
Pivoting The `pivot` method allows a DataFrame to be transformed easily between long and wide formats in the same way as a pivot table is created in a spreadsheet. It takes three arguments: `index`, `columns` and `values`, corresponding to the DataFrame index (the row headers), columns and cell values, respectively. F... | cdystonia.pivot(index='patient', columns='obs', values='twstrs').head() | _____no_output_____ | CC-BY-3.0 | Lecture 4/Lecture 4 - Pandas II (Template).ipynb | iEvidently/ihme-python-course |
If we omit the `values` argument, we get a `DataFrame` with hierarchical columns, just as when we applied `unstack` to the hierarchically-indexed table: A related method, `pivot_table`, creates a spreadsheet-like table with a hierarchical index, and allows the values of the table to be populated using an arbitrary aggr... | cdystonia.head()
cdystonia.pivot_table(index=['site', 'treat'], columns='week', values='twstrs',
aggfunc=max).head(20) | _____no_output_____ | CC-BY-3.0 | Lecture 4/Lecture 4 - Pandas II (Template).ipynb | iEvidently/ihme-python-course |
PrefacedescriptionAbout notebookLoad librariesLoad DatasetColumn DescriptionCleaning DatasetEDA- Null values- Via which channel did user visited - Mobile users- Browser based - Device Category- Operating system- Continent Based- Metro Based- Network Domain- Region- Country Based- Sub Continent Based- Page view V/S bou... | import numpy as np
import pandas as pd
import plotly.graph_objs as go
import plotly.offline as py
from plotly.offline import init_notebook_mode, iplot, download_plotlyjs
import plotly.graph_objs as go
from plotly import tools
import matplotlib.pyplot as plt
init_notebook_mode(connected=True)
from plotly.tools import Fi... | _____no_output_____ | MIT | 9 google customer revenue prediction/exploratory-google-store-analysis.ipynb | MLVPRASAD/KaggleProjects |
Load Dataset | train = pd.read_csv("../input/train.csv")
test = pd.read_csv("../input/test.csv")
# train_df = pd.read_csv('flatten_train.csv')
# test_df = pd.read_csv('flatten_test.csv')
# helper functions
def constant_cols(df):
cols = []
columns = df.columns.values
for col in columns:
if df[col].nunique(dropna = ... | _____no_output_____ | MIT | 9 google customer revenue prediction/exploratory-google-store-analysis.ipynb | MLVPRASAD/KaggleProjects |
Column Description - fullVisitorId- A unique identifier for each user of the Google Merchandise Store.- channelGrouping - The channel via which the user came to the Store.- date - The date on which the user visited the Store.- device - The specifications for the device used to access the Store.- geoNetwork - This sect... | def load_df(csv_path='../input/train.csv', nrows=None):
JSON_COLUMNS = ['device', 'geoNetwork', 'totals', 'trafficSource']
df = pd.read_csv(csv_path,
converters={column: json.loads for column in JSON_COLUMNS},
dtype={'fullVisitorId': 'str'}, # Important!!
... | _____no_output_____ | MIT | 9 google customer revenue prediction/exploratory-google-store-analysis.ipynb | MLVPRASAD/KaggleProjects |
Since totals transaction Revenue is what we are going to predict.and there is no campaignCode in test set Cleaning Dataset | train_constants = constant_cols(train_df)
test_constants = constant_cols(test_df)
print(train_constants)
print(test_constants)
train_df["totals.transactionRevenue"] = train_df["totals.transactionRevenue"].astype('float')
train_df['totals.transactionRevenue'] = train_df['totals.transactionRevenue'].fillna(0)
train_df['d... | _____no_output_____ | MIT | 9 google customer revenue prediction/exploratory-google-store-analysis.ipynb | MLVPRASAD/KaggleProjects |
both the df has same cols with constant values lets remove them | train_constants = constant_cols(train_df)
test_constants = constant_cols(test_df)
train_df = train_df.drop(columns=train_constants,axis = 1)
test_df = test_df.drop(columns=test_constants, axis = 1) | _____no_output_____ | MIT | 9 google customer revenue prediction/exploratory-google-store-analysis.ipynb | MLVPRASAD/KaggleProjects |
EDA Null values | null_values = train_df.isna().sum(axis = 0).reset_index()
null_values = null_values[null_values[0] > 50]
null_chart = [go.Bar(y = null_values['index'],x = null_values[0]*100/len(train_df), orientation = 'h')]
py.iplot(null_chart) | _____no_output_____ | MIT | 9 google customer revenue prediction/exploratory-google-store-analysis.ipynb | MLVPRASAD/KaggleProjects |
**Summary**- So many coloumns has null values- we will find why these columns are null and we will also see how we can manage them. Via which channel did user visited | data = train_df[['channelGrouping','totals.transactionRevenue']]
temp = data['channelGrouping'].value_counts()
chart = [go.Pie(labels = temp.index, values = temp.values)]
py.iplot(chart) | _____no_output_____ | MIT | 9 google customer revenue prediction/exploratory-google-store-analysis.ipynb | MLVPRASAD/KaggleProjects |
**Summary**- Most of the users came via organic search.- Paid search and affilate users are very less. Mobile users | temp = train_df['device.isMobile'].value_counts()
chart = go.Bar(x = ["False","True"], y = temp.values)
py.iplot([chart]) | _____no_output_____ | MIT | 9 google customer revenue prediction/exploratory-google-store-analysis.ipynb | MLVPRASAD/KaggleProjects |
**Summary**- Many users browse the site from desktop or tablet Browser based | count_mean('device.browser',"#7FDBFF","#3D9970") | _____no_output_____ | MIT | 9 google customer revenue prediction/exploratory-google-store-analysis.ipynb | MLVPRASAD/KaggleProjects |
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