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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Let's merge the mask and depths | merged = train_mask.merge(depth, how='left')
merged.head()
plt.figure(figsize=(12, 6))
plt.scatter(merged['salt_proportion'], merged['z'])
plt.title('Proportion of salt vs depth')
print("Correlation: ", np.corrcoef(merged['salt_proportion'], merged['z'])[0, 1]) | Correlation: 0.10361580365557428
| MIT | kaggle_tgs_salt_identification.ipynb | JacksonIsaac/colab_notebooks |
Setup Keras and Train | from keras.models import Model, load_model
from keras.layers import Input
from keras.layers.core import Lambda, RepeatVector, Reshape
from keras.layers.convolutional import Conv2D, Conv2DTranspose
from keras.layers.pooling import MaxPooling2D
from keras.layers.merge import concatenate
from keras.callbacks import EarlyS... | replace test/images/8cf16aa0f5.png? [y]es, [n]o, [A]ll, [N]one, [r]ename: N
| MIT | kaggle_tgs_salt_identification.ipynb | JacksonIsaac/colab_notebooks |
PredictRef: https://www.kaggle.com/jesperdramsch/intro-to-seismic-salt-and-how-to-geophysics | path_test='./test/'
test_ids = next(os.walk(path_test+"images"))[2]
X_test = np.zeros((len(test_ids), im_height, im_width, im_chan), dtype=np.uint8)
X_test_feat = np.zeros((len(test_ids), n_features), dtype=np.float32)
sizes_test = []
print('Getting and resizing test images ... ')
sys.stdout.flush()
for n, id_ in tq... | Successfully submitted to TGS Salt Identification Challenge | MIT | kaggle_tgs_salt_identification.ipynb | JacksonIsaac/colab_notebooks |
PredictRef: https://www.kaggle.com/shaojiaxin/u-net-with-simple-resnet-blocks | callbacks = [
EarlyStopping(patience=5, verbose=1),
ReduceLROnPlateau(patience=3, verbose=1),
ModelCheckpoint('model-tgs-salt-new-1.h5', verbose=1, save_best_only=True, save_weights_only=True)
]
#results = model.fit({'img': [X_train, X_train], 'feat': X_feat_train}, y_train, batch_size=16, epochs=50, callba... | _____no_output_____ | MIT | kaggle_tgs_salt_identification.ipynb | JacksonIsaac/colab_notebooks |
Save output to drive | from google.colab import drive
drive.mount('/content/gdrive')
!ls /content/gdrive/My\ Drive/kaggle_competitions
!cp model-tgs-salt-1.h5 /content/gdrive/My\ Drive/kaggle_competitions/tgs_salt/
!cp model-tgs-salt-2.h5 /content/gdrive/My\ Drive/kaggle_competitions/tgs_salt/
!cp submission.csv /content/gdrive/My\ Drive/kag... | _____no_output_____ | MIT | kaggle_tgs_salt_identification.ipynb | JacksonIsaac/colab_notebooks |
Laboratory 18: Linear Regression Full name: R: HEX: Title of the notebook Date:  The human brain is amazing and mysterious in many ways. Have a look at these sequences. You, with the assistance of your brain, can guess the next ite... | # Load the necessary packages
import numpy as np
import pandas as pd
import statistics
from matplotlib import pyplot as plt
# Create a dataframe:
time = [0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0]
speed = [0, 3, 7, 12, 20, 30, 45.6, 60.3, 77.7, 97.3, 121.2]
data = pd.DataFrame({'Time':time, 'Speed':speed})... | _____no_output_____ | CC0-1.0 | 1-Lessons/Lesson19/Lab19/.src/Lab19_WS.ipynb | dustykat/engr-1330-psuedo-course |
Now, let's explore the data: | data.describe()
time_var = statistics.variance(time)
speed_var = statistics.variance(speed)
print("Variance of recorded times is ",time_var)
print("Variance of recorded times is ",speed_var) | Variance of recorded times is 11.0
Variance of recorded times is 1697.7759999999998
| CC0-1.0 | 1-Lessons/Lesson19/Lab19/.src/Lab19_WS.ipynb | dustykat/engr-1330-psuedo-course |
Is there a relationship ( based on covariance, correlation) between time and speed? | # To find the covariance
data.cov()
# To find the correlation among the columns
# using pearson method
data.corr(method ='pearson') | _____no_output_____ | CC0-1.0 | 1-Lessons/Lesson19/Lab19/.src/Lab19_WS.ipynb | dustykat/engr-1330-psuedo-course |
Let's do linear regression with primitive Python: To estimate "y" using the OLS method, we need to calculate "xmean" and "ymean", the covariance of X and y ("xycov"), and the variance of X ("xvar") before we can determine the values for alpha and beta. In our case, X is time and y is Speed. | # Calculate the mean of X and y
xmean = np.mean(time)
ymean = np.mean(speed)
# Calculate the terms needed for the numator and denominator of beta
data['xycov'] = (data['Time'] - xmean) * (data['Speed'] - ymean)
data['xvar'] = (data['Time'] - xmean)**2
# Calculate beta and alpha
beta = data['xycov'].sum() / data['xvar... | alpha = -16.78636363636363
beta = 11.977272727272727
| CC0-1.0 | 1-Lessons/Lesson19/Lab19/.src/Lab19_WS.ipynb | dustykat/engr-1330-psuedo-course |
We now have an estimate for alpha and beta! Our model can be written as Yₑ = 11.977 X -16.786, and we can make predictions: | X = np.array(time)
ypred = alpha + beta * X
print(ypred) | [-16.78636364 -4.80909091 7.16818182 19.14545455 31.12272727
43.1 55.07727273 67.05454545 79.03181818 91.00909091
102.98636364]
| CC0-1.0 | 1-Lessons/Lesson19/Lab19/.src/Lab19_WS.ipynb | dustykat/engr-1330-psuedo-course |
Let’s plot our prediction ypred against the actual values of y, to get a better visual understanding of our model: | # Plot regression against actual data
plt.figure(figsize=(12, 6))
plt.plot(X, ypred, color="red") # regression line
plt.plot(time, speed, 'ro', color="blue") # scatter plot showing actual data
plt.title('Actual vs Predicted')
plt.xlabel('Time (s)')
plt.ylabel('Speed (m/s)')
plt.show() | _____no_output_____ | CC0-1.0 | 1-Lessons/Lesson19/Lab19/.src/Lab19_WS.ipynb | dustykat/engr-1330-psuedo-course |
The red line is our line of best fit, Yₑ = 11.977 X -16.786. We can see from this graph that there is a positive linear relationship between X and y. Using our model, we can predict y from any values of X! For example, if we had a value X = 20, we can predict that: | ypred_20 = alpha + beta * 20
print(ypred_20) | 222.7590909090909
| CC0-1.0 | 1-Lessons/Lesson19/Lab19/.src/Lab19_WS.ipynb | dustykat/engr-1330-psuedo-course |
Linear Regression with statsmodels: First, we use statsmodels’ ols function to initialise our simple linear regression model. This takes the formula y ~ X, where X is the predictor variable (Time) and y is the output variable (Speed). Then, we fit the model by calling the OLS object’s fit() method. | import statsmodels.formula.api as smf
# Initialise and fit linear regression model using `statsmodels`
model = smf.ols('Speed ~ Time', data=data)
model = model.fit() | _____no_output_____ | CC0-1.0 | 1-Lessons/Lesson19/Lab19/.src/Lab19_WS.ipynb | dustykat/engr-1330-psuedo-course |
We no longer have to calculate alpha and beta ourselves as this method does it automatically for us! Calling model.params will show us the model’s parameters: | model.params | _____no_output_____ | CC0-1.0 | 1-Lessons/Lesson19/Lab19/.src/Lab19_WS.ipynb | dustykat/engr-1330-psuedo-course |
In the notation that we have been using, α is the intercept and β is the slope i.e. α =-16.786364 and β = 11.977273. | # Predict values
speed_pred = model.predict()
# Plot regression against actual data
plt.figure(figsize=(12, 6))
plt.plot(data['Time'], data['Speed'], 'o') # scatter plot showing actual data
plt.plot(data['Time'], speed_pred, 'r', linewidth=2) # regression line
plt.xlabel('Time (s)')
plt.ylabel('Speed (m/s)... | _____no_output_____ | CC0-1.0 | 1-Lessons/Lesson19/Lab19/.src/Lab19_WS.ipynb | dustykat/engr-1330-psuedo-course |
How good do you feel about this predictive model? Will you trust it? Example 2: Advertising and Sells! This is a classic regression problem. we have a dataset of the spendings on TV, Radio, and Newspaper advertisements and number of sales for a specific product. We are interested in exploring the relationship betwee... | # Import and display first rows of the advertising dataset
df = pd.read_csv('advertising.csv')
df.head()
# Describe the df
df.describe()
tv = np.array(df['TV'])
radio = np.array(df['Radio'])
newspaper = np.array(df['Newspaper'])
newspaper = np.array(df['Sales'])
# Get Variance and Covariance - What can we infer?
df.co... | _____no_output_____ | CC0-1.0 | 1-Lessons/Lesson19/Lab19/.src/Lab19_WS.ipynb | dustykat/engr-1330-psuedo-course |
 *This notebook was inspired by a several blogposts including:* - __"Introduction to Linear Regression in Python"__ by __Lorraine Li__ available at* https://towardsdatascience.com/introduction-to-linear-regression-in-python-c12a072bedf0 - __"In Depth: Lin... | # Step1:
vdf = pd.read_csv('CarsDF.csv')
vdf.head()
vdf.describe() | _____no_output_____ | CC0-1.0 | 1-Lessons/Lesson19/Lab19/.src/Lab19_WS.ipynb | dustykat/engr-1330-psuedo-course |
On Step1: [Double-Click to edit] | # Step2:.
vdf.corr() | _____no_output_____ | CC0-1.0 | 1-Lessons/Lesson19/Lab19/.src/Lab19_WS.ipynb | dustykat/engr-1330-psuedo-course |
On Step2: [Double-Click to edit] | #Step3:
# Calculate the mean of X and y
xmean = np.mean(vdf['Age'])
ymean = np.mean(vdf['selling_price'])
# Calculate the terms needed for the numator and denominator of beta
vdf['xycov'] = (vdf['Age'] - xmean) * (vdf['selling_price'] - ymean)
vdf['xvar'] = (vdf['Age'] - xmean)**2
# Calculate beta and alpha
beta = vd... | _____no_output_____ | CC0-1.0 | 1-Lessons/Lesson19/Lab19/.src/Lab19_WS.ipynb | dustykat/engr-1330-psuedo-course |
On Step3: [Double-Click to edit] | # Step4:
import statsmodels.formula.api as smf
# Initialise and fit linear regression model using `statsmodels`
model = smf.ols('selling_price ~ FuelEconomy_kmpl', data=vdf)
model = model.fit()
model.params
# Predict values
FE_pred = model.predict()
# Plot regression against actual data
plt.figure(figsize=(12, 6))
pl... | _____no_output_____ | CC0-1.0 | 1-Lessons/Lesson19/Lab19/.src/Lab19_WS.ipynb | dustykat/engr-1330-psuedo-course |
On Step4: [Double-Click to edit] | # Step5:
import statsmodels.formula.api as smf
# Initialise and fit linear regression model using `statsmodels`
model = smf.ols('selling_price ~ engine_v', data=vdf)
model = model.fit()
model.params
# Predict values
EV_pred = model.predict()
# Plot regression against actual data
plt.figure(figsize=(12, 6))
plt.plot(v... | _____no_output_____ | CC0-1.0 | 1-Lessons/Lesson19/Lab19/.src/Lab19_WS.ipynb | dustykat/engr-1330-psuedo-course |
On Step5: [Double-Click to edit] On Step6: [Double-Click to edit] | #Step7:
# Multiple Linear Regression with scikit-learn:
from sklearn.linear_model import LinearRegression
# Build linear regression model using TV,Radio and Newspaper as predictors
# Split data into predictors X and output Y
predictors = ['Age', 'km_driven', 'FuelEconomy_kmpl','engine_p','engine_v']
X = vdf[predictors... | [900102.89014124]
| CC0-1.0 | 1-Lessons/Lesson19/Lab19/.src/Lab19_WS.ipynb | dustykat/engr-1330-psuedo-course |
Import packages | import os
import sys
import time
from datetime import datetime
import GPUtil
import psutil
#######################
# run after two days
# time.sleep(172800)
#######################
os.environ["CUDA_VISIBLE_DEVICES"] = "0,1,2,3"
sys.path.append("../")
def gpu_free(max_gb):
gpu_id = GPUtil.getFirstAvailable(
... | -- total_GPU_memory: 10.761GB;init_GPU_memoryFree:10.760GB init_GPU_load:0.000% GPU_memoryUtil:0% GPU_memoryUsed:0.001GB
GPU memery and main memery availale, start a job
-- total_GPU_memory: 10.761GB;init_GPU_memoryFree:10.757GB init_GPU_load:0.000% GPU_memoryUtil:0% GPU_memoryUsed:0.004GB
GPU memery and main memery av... | MIT | demo_control_side_sep_16.ipynb | mengzaiqiao/TVBR |
Checking whether the files are scanned images or true pdfs | def is_image(file_path):
with open(file_path, "rb") as f:
return pdftotext.PDF(f)
print(is_image(filename)) | _____no_output_____ | FTL | tasks/extract_text/notebooks/text_preprocessing_jordi.ipynb | jordiplanascuchi/policy-data-analyzer |
Converting pdf to image files and improving quality | def get_image1(file_path):
"""Get image out of pdf file_path. Splits pdf file into PIL images of each of its pages.
"""
return convert_from_path(file_path, 500)
# Performance tips according to pdf2image:
# Using an output folder is significantly faster if you are using an SSD. Otherwise i/o usually becomes the ... | _____no_output_____ | FTL | tasks/extract_text/notebooks/text_preprocessing_jordi.ipynb | jordiplanascuchi/policy-data-analyzer |
What can we do here to improve image quality? It already seems pretty good! Evaluating extraction time from each method and saving text to disk | def export_ocr(text, file, extract, out=out_path):
""" Export ocr output text using extract method to file at out
"""
filename = f'{os.path.splitext(os.path.basename(file))[0]}_{extract}.txt'
with open(os.path.join(out, filename), 'w') as the_file:
the_file.write(text)
def wrap_pagenum(page_text, page_num)... | _____no_output_____ | FTL | tasks/extract_text/notebooks/text_preprocessing_jordi.ipynb | jordiplanascuchi/policy-data-analyzer |
It seems that the pytesseract package provides the fastest extraction and by looking at the extracted text it doesn't seem to exist any difference in the output of all the tested methods. | # comparison between text extracted by the different methods
os.listdir(out_path)
# TODO: perform a more programatical comparison between extracted texts | _____no_output_____ | FTL | tasks/extract_text/notebooks/text_preprocessing_jordi.ipynb | jordiplanascuchi/policy-data-analyzer |
Let's look at the extracted text | with open(os.path.join(out_path, 'Decreto_ejecutivo_57_pytesseract.txt')) as text:
extracted_text = text.read()
extracted_text
# Replace \x0c (page break) by \n
# Match 1 or more occurrences of \n if preceeded by one occurrence of \n OR
# Match 1 or more occurrences of \s (whitespace) if preceeded by one occurrence ... | _____no_output_____ | FTL | tasks/extract_text/notebooks/text_preprocessing_jordi.ipynb | jordiplanascuchi/policy-data-analyzer |
CS109A Introduction to Data Science Standard Section 3: Multiple Linear Regression and Polynomial Regression **Harvard University****Fall 2019****Instructors**: Pavlos Protopapas, Kevin Rader, and Chris Tanner**Section Leaders**: Marios Mattheakis, Abhimanyu (Abhi) Vasishth, Robbert (Rob) Struyven | #RUN THIS CELL
import requests
from IPython.core.display import HTML
styles = requests.get("http://raw.githubusercontent.com/Harvard-IACS/2018-CS109A/master/content/styles/cs109.css").text
HTML(styles) | _____no_output_____ | MIT | content/sections/section3/notebook/cs109a_section_3.ipynb | lingcog/2019-CS109A |
For this section, our goal is to get you familiarized with Multiple Linear Regression. We have learned how to model data with kNN Regression and Simple Linear Regression and our goal now is to dive deep into Linear Regression.Specifically, we will: - Load in the titanic dataset from seaborn- Learn a few ways to plo... | # Data and Stats packages
import numpy as np
import pandas as pd
# Visualization packages
import matplotlib.pyplot as plt
import seaborn as sns
sns.set() | _____no_output_____ | MIT | content/sections/section3/notebook/cs109a_section_3.ipynb | lingcog/2019-CS109A |
Extending Linear Regression Working with the Titanic Dataset from SeabornFor our dataset, we'll be using the passenger list from the Titanic, which famously sank in 1912. Let's have a look at the data. Some descriptions of the data are at https://www.kaggle.com/c/titanic/data, and here's [how seaborn preprocessed it](... | # Load the dataset from seaborn
titanic = sns.load_dataset("titanic")
titanic.head()
# checking for null values
chosen_vars = ['age', 'sex', 'class', 'embark_town', 'alone', 'fare']
titanic = titanic[chosen_vars]
titanic.info() | <class 'pandas.core.frame.DataFrame'>
RangeIndex: 891 entries, 0 to 890
Data columns (total 6 columns):
age 714 non-null float64
sex 891 non-null object
class 891 non-null category
embark_town 889 non-null object
alone 891 non-null bool
fare 891 non-null float64
dtyp... | MIT | content/sections/section3/notebook/cs109a_section_3.ipynb | lingcog/2019-CS109A |
**Exercise**: check the datatypes of each column and display the statistics (min, max, mean and any others) for all the numerical columns of the dataset. | ## your code here
# %load 'solutions/sol1.py'
print(titanic.dtypes)
titanic.describe() | age float64
sex object
class category
embark_town object
alone bool
fare float64
dtype: object
| MIT | content/sections/section3/notebook/cs109a_section_3.ipynb | lingcog/2019-CS109A |
**Exercise**: drop all the non-null *rows* in the dataset. Is this always a good idea? | ## your code here
# %load 'solutions/sol2.py'
titanic = titanic.dropna(axis=0)
titanic.info() | <class 'pandas.core.frame.DataFrame'>
Int64Index: 712 entries, 0 to 890
Data columns (total 6 columns):
age 712 non-null float64
sex 712 non-null object
class 712 non-null category
embark_town 712 non-null object
alone 712 non-null bool
fare 712 non-null float64
dtyp... | MIT | content/sections/section3/notebook/cs109a_section_3.ipynb | lingcog/2019-CS109A |
Now let us visualize the response variable. A good visualization of the distribution of a variable will enable us to answer three kinds of questions:- What values are central or typical? (e.g., mean, median, modes)- What is the typical spread of values around those central values? (e.g., variance/stdev, skewness)- What... | fig, ax = plt.subplots(1, 3, figsize=(24, 6))
ax = ax.ravel()
sns.distplot(titanic['fare'], ax=ax[0])
# use seaborn to draw distributions
ax[0].set_title('Seaborn distplot')
ax[0].set_ylabel('Normalized frequencies')
sns.violinplot(x='fare', data=titanic, ax=ax[1])
ax[1].set_title('Seaborn violin plot')
ax[1].set_yla... | _____no_output_____ | MIT | content/sections/section3/notebook/cs109a_section_3.ipynb | lingcog/2019-CS109A |
How do we interpret these plots? Train-Test Split | from sklearn.model_selection import train_test_split
titanic_train, titanic_test = train_test_split(titanic, train_size=0.7, random_state=99)
titanic_train = titanic_train.copy()
titanic_test = titanic_test.copy()
print(titanic_train.shape, titanic_test.shape) | (498, 6) (214, 6)
| MIT | content/sections/section3/notebook/cs109a_section_3.ipynb | lingcog/2019-CS109A |
Simple one-variable OLS **Exercise**: You've done this before: make a simple model using the OLS package from the statsmodels library predicting **fare** using **age** using the training data. Name your model `model_1` and display the summary | from statsmodels.api import OLS
import statsmodels.api as sm
# Your code here
# %load 'solutions/sol3.py'
age_ca = sm.add_constant(titanic_train['age'])
model_1 = OLS(titanic_train['fare'], age_ca).fit()
model_1.summary() | _____no_output_____ | MIT | content/sections/section3/notebook/cs109a_section_3.ipynb | lingcog/2019-CS109A |
Dealing with different kinds of variables In general, you should be able to distinguish between three kinds of variables: 1. Continuous variables: such as `fare` or `age`2. Categorical variables: such as `sex` or `alone`. There is no inherent ordering between the different values that these variables can take on. Thes... | titanic_orig = titanic_train.copy() | _____no_output_____ | MIT | content/sections/section3/notebook/cs109a_section_3.ipynb | lingcog/2019-CS109A |
Let us now examine the `sex` column and see the value counts. | titanic_train['sex'].value_counts() | _____no_output_____ | MIT | content/sections/section3/notebook/cs109a_section_3.ipynb | lingcog/2019-CS109A |
**Exercise**: Create a column `sex_male` that is 1 if the passenger is male, 0 if female. The value counts indicate that these are the two options in this particular dataset. Ensure that the datatype is `int`. | # your code here
# %load 'solutions/sol4.py'
# functions that help us create a dummy variable
stratify
titanic_train['sex_male'].value_counts() | _____no_output_____ | MIT | content/sections/section3/notebook/cs109a_section_3.ipynb | lingcog/2019-CS109A |
Do we need a `sex_female` column, or a `sex_others` column? Why or why not?Now, let us look at `class` in greater detail. | titanic_train['class_Second'] = (titanic_train['class'] == 'Second').astype(int)
titanic_train['class_Third'] = 1 * (titanic_train['class'] == 'Third') # just another way to do it
titanic_train.info()
# This function automates the above:
titanic_train_copy = pd.get_dummies(titanic_train, columns=['sex', 'class'], drop_... | _____no_output_____ | MIT | content/sections/section3/notebook/cs109a_section_3.ipynb | lingcog/2019-CS109A |
Linear Regression with More Variables **Exercise**: Fit a linear regression including the new sex and class variables. Name this model `model_2`. Don't forget the constant! | # your code here
# %load 'solutions/sol5.py'
model_2 = sm.OLS(titanic_train['fare'],
sm.add_constant(titanic_train[['age', 'sex_male', 'class_Second', 'class_Third']])).fit()
model_2.summary() | _____no_output_____ | MIT | content/sections/section3/notebook/cs109a_section_3.ipynb | lingcog/2019-CS109A |
Interpreting These Results 1. Which of the predictors do you think are important? Why?2. All else equal, what does being male do to the fare? Going back to the example from class3. What is the interpretation of $\beta_0$ and $\beta_1$? Exploring Interactions | sns.lmplot(x="age", y="fare", hue="sex", data=titanic_train, size=6) | /anaconda3/envs/109a/lib/python3.7/site-packages/seaborn/regression.py:546: UserWarning: The `size` paramter has been renamed to `height`; please update your code.
warnings.warn(msg, UserWarning)
| MIT | content/sections/section3/notebook/cs109a_section_3.ipynb | lingcog/2019-CS109A |
The slopes seem to be different for male and female. What does that indicate?Let us now try to add an interaction effect into our model. | # It seemed like gender interacted with age and class. Can we put that in our model?
titanic_train['sex_male_X_age'] = titanic_train['age'] * titanic_train['sex_male']
model_3 = sm.OLS(
titanic_train['fare'],
sm.add_constant(titanic_train[['age', 'sex_male', 'class_Second', 'class_Third', 'sex_male_X_age']])
)... | _____no_output_____ | MIT | content/sections/section3/notebook/cs109a_section_3.ipynb | lingcog/2019-CS109A |
**What happened to the `age` and `male` terms?** | # It seemed like gender interacted with age and class. Can we put that in our model?
titanic_train['sex_male_X_class_Second'] = titanic_train['age'] * titanic_train['class_Second']
titanic_train['sex_male_X_class_Third'] = titanic_train['age'] * titanic_train['class_Third']
model_4 = sm.OLS(
titanic_train['fare'],... | _____no_output_____ | MIT | content/sections/section3/notebook/cs109a_section_3.ipynb | lingcog/2019-CS109A |
Polynomial Regression  Perhaps we now believe that the fare also depends on the square of age. How would we include this term in our model? | fig, ax = plt.subplots(figsize=(12,6))
ax.plot(titanic_train['age'], titanic_train['fare'], 'o')
x = np.linspace(0,80,100)
ax.plot(x, x, '-', label=r'$y=x$')
ax.plot(x, 0.04*x**2, '-', label=r'$y=c x^2$')
ax.set_title('Plotting Age (x) vs Fare (y)')
ax.set_xlabel('Age (x)')
ax.set_ylabel('Fare (y)')
ax.legend(); | _____no_output_____ | MIT | content/sections/section3/notebook/cs109a_section_3.ipynb | lingcog/2019-CS109A |
**Exercise**: Create a model that predicts fare from all the predictors in `model_4` + the square of age. Show the summary of this model. Call it `model_5`. Remember to use the training data, `titanic_train`. | # your code here
# %load 'solutions/sol6.py'
titanic_train['age^2'] = titanic_train['age'] **2
model_5 = sm.OLS(
titanic_train['fare'],
sm.add_constant(titanic_train[['age', 'sex_male', 'class_Second', 'class_Third', 'sex_male_X_age',
'sex_male_X_class_Second', 'sex_male_X_class_... | _____no_output_____ | MIT | content/sections/section3/notebook/cs109a_section_3.ipynb | lingcog/2019-CS109A |
Looking at All Our Models: Model Selection What has happened to the $R^2$ as we added more features? Does this mean that the model is better? (What if we kept adding more predictors and interaction terms? **In general, how should we choose a model?** We will spend a lot more time on model selection and learn about way... | models = [model_1, model_2, model_3, model_4, model_5]
fig, ax = plt.subplots(figsize=(12,6))
ax.plot([model.df_model for model in models], [model.rsquared for model in models], 'x-')
ax.set_xlabel("Model degrees of freedom")
ax.set_title('Model degrees of freedom vs training $R^2$')
ax.set_ylabel("$R^2$"); | _____no_output_____ | MIT | content/sections/section3/notebook/cs109a_section_3.ipynb | lingcog/2019-CS109A |
**What about the test data?**We added a lot of columns to our training data and must add the same to our test data in order to calculate $R^2$ scores. | # Added features for model 1
# Nothing new to be added
# Added features for model 2
titanic_test = pd.get_dummies(titanic_test, columns=['sex', 'class'], drop_first=True)
# Added features for model 3
titanic_test['sex_male_X_age'] = titanic_test['age'] * titanic_test['sex_male']
# Added features for model 4
titanic_... | _____no_output_____ | MIT | content/sections/section3/notebook/cs109a_section_3.ipynb | lingcog/2019-CS109A |
**Calculating R^2 scores** | from sklearn.metrics import r2_score
r2_scores = []
y_preds = []
y_true = titanic_test['fare']
# model 1
y_preds.append(model_1.predict(sm.add_constant(titanic_test['age'])))
# model 2
y_preds.append(model_2.predict(sm.add_constant(titanic_test[['age', 'sex_male', 'class_Second', 'class_Third']])))
# model 3
y_pred... | /anaconda3/envs/109a/lib/python3.7/site-packages/numpy/core/fromnumeric.py:2389: FutureWarning: Method .ptp is deprecated and will be removed in a future version. Use numpy.ptp instead.
return ptp(axis=axis, out=out, **kwargs)
| MIT | content/sections/section3/notebook/cs109a_section_3.ipynb | lingcog/2019-CS109A |
Regression Assumptions. Should We Even Regress Linearly?  **Question**: What are the assumptions of a linear regression model? We find that the answer to this question can be found on closer examimation of $\epsilon$. What is $\epsilon$? It is assumed that $\epsilon$ i... | # your code here
# %load 'solutions/sol7.py'
# %load 'solutions/sol7.py'
predictors = sm.add_constant(titanic_train[['age', 'sex_male', 'class_Second', 'class_Third', 'sex_male_X_age',
'sex_male_X_class_Second', 'sex_male_X_class_Third', 'age^2']])
y_hat = model_5.predict(predicto... | Mean of residuals: 4.784570776163707e-13
| MIT | content/sections/section3/notebook/cs109a_section_3.ipynb | lingcog/2019-CS109A |
**What can you say about the assumptions of the model?** ---------------- End of Standard Section--------------- Extra: Visual exploration of predictors' correlationsThe dataset for this problem contains 10 simulated predictors and a response variable. | # read in the data
data = pd.read_csv('../data/dataset3.txt')
data.head()
# this effect can be replicated using the scatter_matrix function in pandas plotting
sns.pairplot(data); | _____no_output_____ | MIT | content/sections/section3/notebook/cs109a_section_3.ipynb | lingcog/2019-CS109A |
Predictors x1, x2, x3 seem to be perfectly correlated while predictors x4, x5, x6, x7 seem correlated. | data.corr()
sns.heatmap(data.corr()) | _____no_output_____ | MIT | content/sections/section3/notebook/cs109a_section_3.ipynb | lingcog/2019-CS109A |
Count all the words | wordcounter = Counter({})
words_per_video = []
for ann_idx, ann_file in enumerate(all_annotations):
file = open(ann_file, "r")
words = file.read().split()
file.close()
current_wordcounter = Counter(words)
wordcounter += current_wordcounter
words_per_video.append(len(words))
| _____no_output_____ | MIT | Get Stats.ipynb | jrterven/lip_reading_dataset |
Some stats | print("Number of words:", len(wordcounter))
print("10 most common words:")
print(wordcounter.most_common(10))
print("Max words in a video:", max(words_per_video))
print("Min words in a video:", min(words_per_video))
words_per_video_counter = Counter(words_per_video)
print(words_per_video_counter) | Counter({11: 762, 12: 746, 9: 662, 10: 650, 13: 643, 8: 602, 5: 601, 4: 592, 7: 570, 6: 549, 14: 524, 3: 513, 2: 438, 15: 380, 1: 329, 16: 267, 17: 179, 18: 92, 19: 35, 20: 21, 21: 15, 22: 8, 23: 2, 25: 1, 24: 1})
| MIT | Get Stats.ipynb | jrterven/lip_reading_dataset |
hp tuning | # LogisticRegression, L1
logreg = LogisticRegression(penalty='l1',solver='saga',random_state=0,max_iter=10000)
grid = {'C': np.logspace(-5, 5, 11)}
#predefined splits
#gs = GridSearchCV(logreg, grid, cv=ps.split(),scoring='accuracy')
gs = GridSearchCV(logreg, grid, cv=ps.split(),scoring=['roc_auc','average_precision']... | _____no_output_____ | MIT | models/Linear_ensemble/hyperparameter tuning/linear model_new_classification-seq only.ipynb | jingyi7777/CasRx_guide_efficiency |
Test models | def classification_analysis(model_name, split, y_pred,y_true):
test_df = pd.DataFrame(list(zip(list(y_pred), list(y_true))),
columns =['predicted_value', 'true_binary_label'])
thres_list = [0.8, 0.9,0.95]
tp_thres = []
#print('thres_stats')
for thres in thres_list:
df_pre... | test: ['RPL31', 'RPS3A', 'CSE1L', 'XAB2', 'PSMD7', 'SUPT6H']
test: ['EEF2', 'RPS11', 'SNRPD2', 'RPL37', 'SF3B3', 'DDX51']
test: ['RPL7', 'RPS9', 'KARS', 'SF3A1', 'RPL32', 'PSMB2']
test: ['RPS7', 'EIF4A3', 'U2AF1', 'PSMA1', 'PHB', 'POLR2D']
test: ['RPSA', 'RPL23A', 'NUP93', 'AQR', 'RPA2', 'SUPT5H']
test: ['RPL6', 'RPS13... | MIT | models/Linear_ensemble/hyperparameter tuning/linear model_new_classification-seq only.ipynb | jingyi7777/CasRx_guide_efficiency |
Test functions | from utils.sparse import * | _____no_output_____ | Apache-2.0 | jnotebook/test utils sparse functions.ipynb | edervishaj/spotify-recsys-challenge |
Function list 1. inplace_set_rows_zero_where_sum (X, op, cut) 2. inplace_set_cols_zero_where_sum (X, op, cut)3. inplace_set_rows_zero (X, target_rows)4. inplace_set_cols_zero (X, target_cols)5. inplace_row_scale (X, scale)6. inplace_col_scale (X, scale) 7. sum_cols (X)8. sum_rows (X) | m = sp.random(4,5,0.5).tocsr()
m.data = np.ones(m.data.shape[0])
print(m.todense())
inplace_row_scale(m,np.array([1,2,3,4]))
print (m.todense())
m = sp.random(4,5,0.5).tocsc()
m.data = np.ones(m.data.shape[0])
print(m.todense())
inplace_col_scale(m,np.array([1,2,3,4,5]))
print (m.todense())
m = sp.random(4,5,0.5).tocsr... | [1.96108189 1.12923879 0. 1.93997106 0.40970854]
[[0. 0.69020914 0. 0. 0.40970854]
[0. 0. 0. 0. 0. ]
[0. 0. 0. 0. 0. ]
[0. 0.43902965 0. 0. 0. ]]
| Apache-2.0 | jnotebook/test utils sparse functions.ipynb | edervishaj/spotify-recsys-challenge |
Pivot table- excel에서 보던 것- index축은 groupby와 동일- column에 추가로 labeling값을 추가하여,- Value에 numeric type 값을 aggregation하는 형태 | import dateutil
df_phone = pd.read_csv("code/ch5/data/phone_data.csv")
df_phone['date'] = df_phone['date'].apply(dateutil.parser.parse, dayfirst=True)
df_phone.tail()
df_phone.pivot_table(['duration'], index=['month','item'], columns=['network'], fill_value=0, aggfunc='sum') | _____no_output_____ | MIT | inflearn_machine_learning/pandas/pandas_pivot_crosstab.ipynb | Junhojuno/TIL |
Crosstab- 두 컬럼의 교차 빈도, 비율, 덧셈 등을 구할 때 사용- Pivot table의 특수한 형태- User-Item Rating Matrix 등을 만들 때 사용가능 | df_movie = pd.read_csv("code/ch5/data/movie_rating.csv")
df_movie.tail()
# 평론가의 영화별 평점
pd.crosstab(values=df_movie.rating, index=df_movie.critic, columns=df_movie.title, aggfunc='first').fillna(0)
# 이걸 groupby로 만들어보자.1
df_movie.groupby(['critic','title'])['rating'].first().unstack().fillna(0)
# 이걸 groupby로 만들어보자.2
df_m... | _____no_output_____ | MIT | inflearn_machine_learning/pandas/pandas_pivot_crosstab.ipynb | Junhojuno/TIL |
MNIST Simple DEMO | import argparse
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
class Arguments:
batch = 64
test_batch = 512
epochs = 10
lr = .01
momentum = .5
seed = 42
log_interval = 100
args = Arguments()
class Ne... | Train Epoch: 1 [0/60000 (0%)] Loss: 2.309220
Train Epoch: 1 [6400/60000 (11%)] Loss: 0.545335
Train Epoch: 1 [12800/60000 (21%)] Loss: 0.417650
Train Epoch: 1 [19200/60000 (32%)] Loss: 0.353491
Train Epoch: 1 [25600/60000 (43%)] Loss: 0.306972
Train Epoch: 1 [32000/60000 (53%)] Loss: 0.133229
Train Epoch: 1 [38400/6000... | MIT | legacy/MNIST/lab.ipynb | MaybeS/mnist |
Project 0: Inaugural project Labor Supply Problem Following labor supply problem is given: $$c^*,l^* = log(c) - v \frac{l^{1+\frac{1}{\epsilon}}}{1+\frac{1}{\epsilon}}\\x = m + wl - [\tau_0wl+\tau_1 \max(wl-\kappa,0)]\\c \in [0,x]\\l \in [0,1]\\$$Where: c is consumption,l is labor supply,m is cash-on-hand, w is the wa... | # All used packages are imported
import numpy as np
import sympy as sm
from scipy import optimize
t0 = sm.symbols('t_0')
t1 = sm.symbols('t_1')
m = 1 #cash-on-hand
v = 10 #disutility of labor
e = 0.3 #elasticity of labor supply
t0 = 0.4 #standard labor income tax
t1 = 0.1 #top bracket labor income tax
k = 0.4 #c... | labour supply is:0.31961536193545265
consumption is:1.119903840483863
utility:0.09677772523865749
| MIT | Project 1.ipynb | notnasobe666/BlackHatGang |
Question 2 | import matplotlib.pyplot as plt
plt.style.use('grayscale')
# Plot l_star and c_star with w going from 0.5 to 1.5
# The definitions are defined - the used packages is defined above
N = 10000
w_vector = np.linspace(0.5,1.5,num=N)
c_optimal = np.empty(N)
l_optimal = np.empty(N)
# a loop is generated to test the range ... | _____no_output_____ | MIT | Project 1.ipynb | notnasobe666/BlackHatGang |
Question 3 | # Calculate the tax revenue
tax_revenue = np.sum( t0 * w_vector * l_optimal + t1 * np.max( w_vector * l_optimal - k ,0 ))
print('Total tax revenue is: ' + str(tax_revenue))
| Total tax revenue is: 1775.3896759006836
| MIT | Project 1.ipynb | notnasobe666/BlackHatGang |
Question 4 | # How does the tax revenue change when e = 0.1?
# New epsilon is defined
e_new = 0.1
l_optimal_e_new = np.empty(N)
# Same loop is used as above but only a new labor
# supply is calculated as consumption isn't included
# in the tax revenue formula
for i, w in enumerate(w_vector):
optimization = optimizer(w,e_new,v... | New total tax revenue: 3578.900497991557
The difference in tax revenue is: 1803.5108220908735
| MIT | Project 1.ipynb | notnasobe666/BlackHatGang |
Question 5 | # Optimize the tax
# Same optimization formula as above
def tax_optimize(t0,t1,k):
tax_optimal = optimize.minimize_scalar(tax_revenue , method='bounded' , x=[0.1,0.1,0.1])
t0_optimal = tax_optimal.x
t1_optimal = tax_optimal.x
k_optimal = tax_optimal.x
return t0_optimal, t1_optimal, k_optimal
t0... | _____no_output_____ | MIT | Project 1.ipynb | notnasobe666/BlackHatGang |
Tic-Tac-Toe AgentIn this notebook, you will learn to build an RL agent (using Q-learning) that learns to play Numerical Tic-Tac-Toe with odd numbers. The environment is playing randomly with the agent, i.e. its strategy is to put an even number randomly in an empty cell. The following is the layout of the notebook: ... | # from <TC_Env> import <TicTacToe> - import your class from environment file
from TCGame_Env import TicTacToe
import collections
import numpy as np
import random
import pickle
import time
from matplotlib import pyplot as plt
from tqdm import tqdm
# Function to convert state array into a string to store it as keys in th... | _____no_output_____ | MIT | TicTacToe_Agent.ipynb | Chiragchhillar1/ML-TicTacToe |
Epsilon-greedy strategy - Write your code here(you can build your epsilon-decay function similar to the one given at the end of the notebook) | # Defining epsilon-greedy policy. You can choose any function epsilon-decay strategy
def epsilon_greedy(state, time):
max_epsilon = 1.0
min_epsilon = 0.001
epsilon = min_epsilon + (max_epsilon - min_epsilon) * np.exp(-0.000001*time)
z = np.random.random()
if z > epsilon:
action = max... | _____no_output_____ | MIT | TicTacToe_Agent.ipynb | Chiragchhillar1/ML-TicTacToe |
Tracking the state-action pairs for checking convergence - write your code here | # Initialise Q_dictionary as 'Q_dict' and States_tracked as 'States_track' (for convergence)
Q_dict = collections.defaultdict(dict)
States_track = collections.defaultdict(dict)
print(len(Q_dict))
print(len(States_track))
# Initialise states to be tracked
def initialise_tracking_states():
sample_q_values = [... | _____no_output_____ | MIT | TicTacToe_Agent.ipynb | Chiragchhillar1/ML-TicTacToe |
Define hyperparameters ---write your code here | EPISODES = 6000000
LR = 0.20
GAMMA = 0.8
threshold = 2540
checkpoint_print_episodes = 600000 | _____no_output_____ | MIT | TicTacToe_Agent.ipynb | Chiragchhillar1/ML-TicTacToe |
Q-update loop ---write your code here | start_time = time.time()
q_track={}
q_track['x-3-x-x-x-6-x-x-x']=[]
q_track['x-1-x-x-x-x-8-x-x']=[]
q_track['x-x-x-x-6-x-x-x-5']=[]
q_track['x-x-x-x-9-x-6-x-x']=[]
q_track['x-5-x-2-x-x-4-7-x']=[]
q_track['9-x-5-x-x-x-8-x-4']=[]
q_track['2-7-x-x-6-x-x-3-x']=[]
q_track['9-x-x-x-x-2-x-x-x']=[]
q_track['x-x-7-x-x-x-x-x-2'... | _____no_output_____ | MIT | TicTacToe_Agent.ipynb | Chiragchhillar1/ML-TicTacToe |
Check the Q-dictionary | Q_dict
len(Q_dict)
# try checking for one of the states - that which action your agent thinks is the best -----This will not be evaluated
Q_dict['x-x-5-x-x-x-x-x-4'] | _____no_output_____ | MIT | TicTacToe_Agent.ipynb | Chiragchhillar1/ML-TicTacToe |
Check the states tracked for Q-values convergence(non-evaluative) | # Write the code for plotting the graphs for state-action pairs tracked
plt.figure(0, figsize=(16,7))
plt.subplot(241)
t1=States_track['x-3-x-x-x-6-x-x-x'][(0,1)]
plt.title("(s,a)=('x-3-x-x-x-6-x-x-x',(0,1))")
plt.plot(np.asarray(range(0, len(t1))),np.asarray(t1))
plt.subplot(242)
t2=States_track['x-x-x-x-6-x-x-x-5'][... | _____no_output_____ | MIT | TicTacToe_Agent.ipynb | Chiragchhillar1/ML-TicTacToe |
Epsilon - decay check | max_epsilon = 1.0
min_epsilon = 0.001
time = np.arange(0,5000000)
epsilon = []
for i in range(0,5000000):
epsilon.append(min_epsilon + (max_epsilon - min_epsilon) * np.exp(-0.000001*i))
plt.plot(time, epsilon)
plt.show() | _____no_output_____ | MIT | TicTacToe_Agent.ipynb | Chiragchhillar1/ML-TicTacToe |
[fnmatch](https://docs.python.org/3/library/fnmatch.html)1. What is fnmatch and why is it useful?1. Why should I use fnmatch and not regex?1. Two examplesFnmatch is part of the python standard library. Allows the use of UNIX style wildcards for string matching. Makes it easy to select a single file type out of a list ... | import fnmatch
FILES = ["some_picture.png", "some_data.csv", "another_picture.png"]
# select only the .png files
for file in FILES:
if fnmatch.fnmatch(file, '*.png'):
print(file)
# or using the fnmatch shorthand
print(fnmatch.filter(FILES, '*.png')) | some_picture.png
another_picture.png
['some_picture.png', 'another_picture.png']
| MIT | 2021-06-09-fnmatch.ipynb | phackstock/code-and-tell |
*SIDE NOTE*: The matching is **case insensitive**, if you want to perform a case sensitive match use [`fnmatch.fnmatchcase()`](https://docs.python.org/3/library/fnmatch.htmlfnmatch.fnmatchcase) Match a list of patterns | MODELS = ["MESSAGEix-GLOBIOM 1.0",
"MESSAGEix-GLOBIOM 1.1",
"REMIND-MAgPIE 2.1-4.2",
"REMIND-MAgPIE 1.7-3.2",
"NIGEM",
"POLES GECO2019",
"COFFEE 1.0",
"COFFEE 2.0",
"TEA",
"GCAM5.2",
"GCAM5.3"]
MATCH_MODELS = ["MESSAGEi... | MESSAGEix-GLOBIOM 1.0
MESSAGEix-GLOBIOM 1.1
REMIND-MAgPIE 2.1-4.2
REMIND-MAgPIE 1.7-3.2
| MIT | 2021-06-09-fnmatch.ipynb | phackstock/code-and-tell |
Question 6:Write a code in python to display different functions of python module. | #module required
import time
print("I am Iron Man.")
time.sleep(2.4)#this function delays the time
print("I love you 3000.") #this statement is printed after 2.4 seconds
import time
# seconds passed since epoch
seconds = 1545925769.9618232
local_time = time.ctime(seconds)
print("Local time:", local_time) | _____no_output_____ | MIT | Python/C6.ipynb | pooja-gera/TheWireUsChallenge |
**3.d Formación de vectores****Responsable:**César Zamora Martínez**Infraestructura usada:** Google Colab, para pruebas 0. Importamos librerias necesarias**Fuente:** 3c_formacion_matrices.ipynb, 3c_formacion_abc.ipynb, 3c_formacion_delta.ipynb | !curl https://colab.chainer.org/install | sh -
import cupy as cp
def formar_vectores(mu, Sigma):
'''
Calcula las cantidades u = \Sigma^{-1} \mu y v := \Sigma^{-1} \cdot 1 del problema de Markowitz
Args:
mu (cupy array, vector): valores medios esperados de activos (dimension n)
Sigma (cupy array, matriz... | _____no_output_____ | RSA-MD | notebooks/Programacion/3d_formacion_vectores.ipynb | izmfc/MNO_finalproject |
1. Implementación**Consideraciones:**. Esta etapa supone que se conocen $\bar{r}$, $\mu$ y $\Sigma$ asociados a los activos, ello con el objeto de es obtener valores escalares que serán relevantes para obtener los pesos del portafolio para el inversionista. Hasta este punto se asume que ya conocemos todos los términos... | def formar_omegas(r, mu, Sigma):
'''
Calcula las cantidades w_o y w_1 del problema de Markowitz
(valores de multiplicadores de Lagrange)
Args:
r (cupy array, escalar): escalar que denota el retorno esperado por el inversionista
mu (cupy array, vector): valores medios esperados de activos (dimension n)
... | _____no_output_____ | RSA-MD | notebooks/Programacion/3d_formacion_vectores.ipynb | izmfc/MNO_finalproject |
1.1 Valores de prueba | n= 10
# r y mu
r= 10
mu=cp.random.rand(n, 1)
# Sigma
S=cp.random.rand(n, n)
Sigma=S@S
# multiplicadores de lagrande
formar_omegas(r,mu,Sigma) | _____no_output_____ | RSA-MD | notebooks/Programacion/3d_formacion_vectores.ipynb | izmfc/MNO_finalproject |
OverviewThis notebook works on the IEEE-CIS Fraud Detection competition. Here I build a simple XGBoost model based on a balanced dataset. Lessons:. keep the categorical variables as single items. Use a high max_depth for xgboost (maybe 40) Ideas to try:. train divergence of expected value (eg. for TransactionAmt and ... | # all imports necessary for this notebook
%matplotlib inline
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import random
import gc
import copy
import missingno as msno
import xgboost
from xgboost import XGBClassifier, XGBRegressor
from sklearn.model_selection import StratifiedKFold, cross_vali... | _____no_output_____ | MIT | ieee-preprocess-v2-0-top-300.ipynb | tarekoraby/IEEE-CIS-Fraud-Detection |
Load the iris data | import matplotlib.pyplot as plt
%matplotlib inline
from sklearn.datasets import load_iris
from numpy.linalg import inv
import pandas as pd
import numpy as np
iris = load_iris()
iris['data'][:5,:]
y = np.where(iris['target'] == 2, 1, 0)
X = iris['data']
const = np.ones(shape=y.shape).reshape(-1,1)
mat = np.concatena... | _____no_output_____ | MIT | logistic-regression/gradient-descent-logistic-regression.ipynb | appliedecon/data602-lectures |
Recall the algorithm we created for gradient descent for linear regressionUsing the following cost function:$$J(w)=\frac{1}{2}\sum(y^{(i)} - \hat{y}^{(i)})^2$$ | import numpy as np
def gradientDescent(x, y, theta, alpha, m, numIterations):
thetaHistory = list()
xTrans = x.transpose()
costList = list()
for i in range(0, numIterations):
# data x feature weights = y_hat
hypothesis = np.dot(x, theta)
# how far we are off
lo... | _____no_output_____ | MIT | logistic-regression/gradient-descent-logistic-regression.ipynb | appliedecon/data602-lectures |
For Logistic regression we replace with our likehihood function:$$J(w)=\sum{[-y^{(i)}log(\theta(z^{(i)}))-(1-y^{(i)})log(1-\theta(z^{(i)})]}$$ And add the sigmoid function to bound $y$ between 0 and 1 | def gradientDescent(x, y, alpha, numIterations):
def mle(y,yhat):
'''
This replaces the mean squared error
'''
return (-y.dot(np.log(yhat)) - ((1-y)).dot(np.log(1-yhat)))
def sigmoid(z):
'''
Transforms values to follow the sigmoid function and bound between 0... | _____no_output_____ | MIT | logistic-regression/gradient-descent-logistic-regression.ipynb | appliedecon/data602-lectures |
Let's try it out- Run the algorithm, which gives us the weight and cost history. - Plot the cost to see if it converges. - Make predictions with the last batch of weights. - Apply the sigmoid function to the above predictions. - Plot the actual vs. predicted values. - Plot the evolution of the weights for each ite... | iters = 500000
import datetime
start_ts = datetime.datetime.now()
betaHistory, costList = gradientDescent(mat, y, alpha=0.01, numIterations=iters)
end_ts = datetime.datetime.now()
print(f'Completed in {end_ts-start_ts}')
# cost history
plt.plot(costL... | Completed in 0:00:17.566409
| MIT | logistic-regression/gradient-descent-logistic-regression.ipynb | appliedecon/data602-lectures |
셀레니움을 이용한 네이버 블로그(검색창) 크롤러- 네이버 메인 검색 페이지에서 크롤링한다. | import platform
print(platform.architecture())
!python --version
pwd
# 네이버에서 검색어 입력받아 검색 한 후 블로그 메뉴를 선택하고
# 오른쪽에 있는 검색옵션 버튼을 눌러서
# 정렬 방식과 기간을 입력하기
#Step 0. 필요한 모듈과 라이브러리를 로딩하고 검색어를 입력 받습니다.
import sys
import os
import pandas as pd
import numpy as np
import math
from bs4 import BeautifulSoup
import requests
import url... | _____no_output_____ | MIT | naversearchCrawlerSelenium.ipynb | JeongCheck/Crawling |
1 page = 7 posts72 page searchsample = https://section.blog.naver.com/Search/Post.naver?pageNo=1&rangeType=PERIOD&orderBy=sim&startDate=2019-01-01&endDate=2021-05-01&keyword=%EC%84%B1%EC%8B%AC%EB%8B%B9%EC%97%AC%ED%96%89%EB%8C%80%EC%A0%84 | ## 제목 눌러서 블로그 페이지 열기
driver.find_element_by_class_name('title').click()
time.sleep(1)
type(searched_post_num), searched_post_num
import re
re.sub('^[0-9]', '', searched_post_num)
searched_post_num
searched_post_num.replace(',', '').replace('건', '')
total_page = math.ceil(int(searched_post_num.replace(',', '').strip('건'... | _____no_output_____ | MIT | naversearchCrawlerSelenium.ipynb | JeongCheck/Crawling |
{ 'mean': [axis1, axis2, flattened], 'variance': [axis1, axis2, flattened], 'standard deviation': [axis1, axis2, flattened], 'max': [axis1, axis2, flattened], 'min': [axis1, axis2, flattened], 'sum': [axis1, axis2, flattened]} | calculations['mean']= [a.mean(axis=0).tolist(), a.mean(axis=1).tolist(), a.mean().tolist()]
calculations['mean']
calculations['variance']= [a.var(axis=0).tolist(), a.var(axis=1).tolist(), a.var().tolist()]
calculations
calculations['standard deviation']= [a.std(axis=0).tolist(), a.std(axis=1).tolist(), a.std().tolist()... | _____no_output_____ | MIT | data_analysis/Mean-Variance-Standard Deviation Calculator.ipynb | alanpirotta/freecodecamp_certif |
Torrent To Google Drive Downloader **Important Note:** To get more disk space:> Go to Runtime -> Change Runtime and give GPU as the Hardware Accelerator. You will get around 384GB to download any torrent you want. Install libtorrent and Initialize Session | !apt install python3-libtorrent
import libtorrent as lt
ses = lt.session()
ses.listen_on(6881, 6891)
downloads = [] | _____no_output_____ | MIT | Torrent_To_Google_Drive_Downloader.ipynb | abhibhaw/Torrent-To-Google-Drive-Downloader |
Mount Google DriveTo stream files we need to mount Google Drive. | from google.colab import drive
drive.mount("/content/drive") | _____no_output_____ | MIT | Torrent_To_Google_Drive_Downloader.ipynb | abhibhaw/Torrent-To-Google-Drive-Downloader |
Add From Torrent FileYou can run this cell to add more files as many times as you want | from google.colab import files
source = files.upload()
params = {
"save_path": "/content/drive/My Drive/Torrent",
"ti": lt.torrent_info(list(source.keys())[0]),
}
downloads.append(ses.add_torrent(params)) | _____no_output_____ | MIT | Torrent_To_Google_Drive_Downloader.ipynb | abhibhaw/Torrent-To-Google-Drive-Downloader |
Add From Magnet LinkYou can run this cell to add more files as many times as you want | params = {"save_path": "/content/drive/My Drive/Torrent"}
while True:
magnet_link = input("Enter Magnet Link Or Type Exit: ")
if magnet_link.lower() == "exit":
break
downloads.append(
lt.add_magnet_uri(ses, magnet_link, params)
)
| _____no_output_____ | MIT | Torrent_To_Google_Drive_Downloader.ipynb | abhibhaw/Torrent-To-Google-Drive-Downloader |
Start DownloadSource: https://stackoverflow.com/a/5494823/7957705 and [3 issue](https://github.com/FKLC/Torrent-To-Google-Drive-Downloader/issues/3) which refers to this [stackoverflow question](https://stackoverflow.com/a/6053350/7957705) | import time
from IPython.display import display
import ipywidgets as widgets
state_str = [
"queued",
"checking",
"downloading metadata",
"downloading",
"finished",
"seeding",
"allocating",
"checking fastresume",
]
layout = widgets.Layout(width="auto")
style = {"description_width": "ini... | _____no_output_____ | MIT | Torrent_To_Google_Drive_Downloader.ipynb | abhibhaw/Torrent-To-Google-Drive-Downloader |
Analysis of enrichment | import glob
import json
import math
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from functools import reduce
from collections import OrderedDict, defaultdict
from sklearn.feature_extraction.text import TfidfVectorizer
from scipy.stats import fisher_exact as fisher
fro... | _____no_output_____ | MIT | scripts/pathways_3_categorization.ipynb | iganna/evo_epigen |
Collecting all pathway names | pathway_tables = glob.glob("../pathways/*/gp.csv")
dfs = [pd.read_csv(table) for table in pathway_tables]
for i, df in enumerate(dfs):
dfs[i] = df.set_index("SYMBOL")
dfs[i].sort_index(inplace=True)
#print(dfs[i].shape)
dfs[0]
all_entries = list(pd.concat(dfs, axis=1, sort=True).columns)
all_entries[0:10]
s... | _____no_output_____ | MIT | scripts/pathways_3_categorization.ipynb | iganna/evo_epigen |
By histone tag: | my_tags = ["H3K4me3", "H3K9ac", "H3K27ac", "H3K27me3", "H3K9me3"]
ENR_COUNTERS = dict()
for hg_tag in my_tags:
files_up_human = glob.glob(f"../extracted/Human_{hg_tag}_pathways_up*")
files_down_human = glob.glob(f"../extracted/Human_{hg_tag}_pathways_down*")
files_up_mouse = glob.glob(f"../extracted/Mouse_{... | _____no_output_____ | MIT | scripts/pathways_3_categorization.ipynb | iganna/evo_epigen |
Calculates a contingency table EASE score [x y] [z k] :param n_in_path: number of outliers in the pathway :param n_total_path: total number of genes in the pathway :param n_outliers: total number of outliers :param n_total: total number of genes analysed :return: | ksi = defaultdict(dict)
signs = {"+": "positively\u00A0enriched\u00A0(+)",
"-": "negatively\u00A0enriched\u00A0(-)"}
for hg_tag in my_tags:
enriched_counter = ENR_COUNTERS[hg_tag]
for sign in ["+", "-"]:
for org in ["Human", "Mouse"]:
for category in enriched_counter:
... | _____no_output_____ | MIT | scripts/pathways_3_categorization.ipynb | iganna/evo_epigen |
Basic usage Thunder offers a variety of analyses and workflows for spatial and temporal data. When run on a cluster, most methods are efficiently and automatically parallelized, but Thunder can also be used on a single machine, especially for testing purposes. We'll walk through a very simple example here as an introd... | data = tsc.loadExample('fish-series') | _____no_output_____ | Apache-2.0 | python/doc/tutorials/src/basic_usage.ipynb | broxtronix/thunder |
``data`` is a ``Series`` object, which is a generic collection of one-dimensional array data sharing a common index. We can inspect it to see metadata: | data | _____no_output_____ | Apache-2.0 | python/doc/tutorials/src/basic_usage.ipynb | broxtronix/thunder |
A ``Series`` object is a collection of key-value records, each containing an identifier as a key and a one-dimensional array as a value. We can look at the first key and value by using ``first()``. | key, value = data.first() | _____no_output_____ | Apache-2.0 | python/doc/tutorials/src/basic_usage.ipynb | broxtronix/thunder |
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