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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Module Project: Ridge RegressionFor this project, you'll return to the Tribecca Condo dataset. But this time, you'll look at the _entire_ dataset and try to predict property sale prices.The [NYC Department of Finance](https://www1.nyc.gov/site/finance/taxes/property-rolling-sales-data.page) has a glossary of property ... | def wrangle(filepath):
# Import csv file
cols = ['BOROUGH', 'NEIGHBORHOOD',
'BUILDING CLASS CATEGORY', 'GROSS SQUARE FEET',
'YEAR BUILT', 'SALE PRICE', 'SALE DATE']
df = pd.read_csv(filepath, usecols=cols)
return df
filepath = DATA_PATH+'condos/NYC_Citywide_Rolling_Calendar_Sa... | _____no_output_____ | MIT | module3-ridge-regression/LS_DS_213_assignment.ipynb | ppmorgoun/DS-Unit-2-Linear-Models |
**Task 1:** Use the above `wrangle` function to import the `NYC_Citywide_Rolling_Calendar_Sales.csv` file into a DataFrame named `df`. | df = ... | _____no_output_____ | MIT | module3-ridge-regression/LS_DS_213_assignment.ipynb | ppmorgoun/DS-Unit-2-Linear-Models |
**Task 2:** Modify the above `wrangle` function so that:- The column `'SALE DATE'` becomes the `DatetimeIndex`.- The dtype for the `'BOROUGH'` column is `object`, not `int`.- The dtype for the `'SALE PRICE'` column is `int`, not `object`.- The dataset includes only one-family dwellings (`BUILDING CLASS CATEGORY == '01 ... | # Perform your exploratory data analysis here and
# modify the wrangle function above | _____no_output_____ | MIT | module3-ridge-regression/LS_DS_213_assignment.ipynb | ppmorgoun/DS-Unit-2-Linear-Models |
II. Split Data**Task 3:** Split your dataset into the feature matrix `X` and the target vector `y`. You want to predict `'SALE_PRICE'`. | X = ...
y = ... | _____no_output_____ | MIT | module3-ridge-regression/LS_DS_213_assignment.ipynb | ppmorgoun/DS-Unit-2-Linear-Models |
**Task 4:** Split `X` and `y` into a training set (`X_train`, `y_train`) and a test set (`X_test`, `y_test`).- Your training set should include data from January to March 2019. - Your test set should include data from April 2019. | X_train, y_train = ..., ...
X_test, y_test = ..., ... | _____no_output_____ | MIT | module3-ridge-regression/LS_DS_213_assignment.ipynb | ppmorgoun/DS-Unit-2-Linear-Models |
III. Establish Baseline**Task 5:** Since this is a **regression** problem, you need to calculate the baseline mean absolute error for your model. | baseline_mae = ...
print('Baseline MAE:', baseline_mae) | _____no_output_____ | MIT | module3-ridge-regression/LS_DS_213_assignment.ipynb | ppmorgoun/DS-Unit-2-Linear-Models |
IV. Build Model **Task 6:** Build and train a `OneHotEncoder` and then use it to transform `X_train` and `X_test`. | ohe = ...
XT_train = ...
XT_test = ... | _____no_output_____ | MIT | module3-ridge-regression/LS_DS_213_assignment.ipynb | ppmorgoun/DS-Unit-2-Linear-Models |
**Task 7:** Build and train a `LinearRegression` model named `model_lr`. Remember to train your model using your _transformed_ feature matrix. | model_lr = ... | _____no_output_____ | MIT | module3-ridge-regression/LS_DS_213_assignment.ipynb | ppmorgoun/DS-Unit-2-Linear-Models |
**Task 8:** Build and train a `Ridge` model named `model_r`. Remember to train your model using your _transformed_ feature matrix. | model_r = ... | _____no_output_____ | MIT | module3-ridge-regression/LS_DS_213_assignment.ipynb | ppmorgoun/DS-Unit-2-Linear-Models |
V. Check Metrics**Task 9:** Check the training and test metrics for `model_lr`. | training_mae_lr = ...
test_mae_lr = ...
print('Linear Training MAE:', training_mae_lr)
print('Linear Test MAE:', test_mae_lr) | _____no_output_____ | MIT | module3-ridge-regression/LS_DS_213_assignment.ipynb | ppmorgoun/DS-Unit-2-Linear-Models |
**Task 10:** Check the training and test metrics for `model_r`. | training_mae_r = ...
test_mae_r = ...
print('Ridge Training MAE:', training_mae_r)
print('Ridge Test MAE:', test_mae_r) | _____no_output_____ | MIT | module3-ridge-regression/LS_DS_213_assignment.ipynb | ppmorgoun/DS-Unit-2-Linear-Models |
**Stretch Goal:** Calculate the training and test $R^2$ scores `model_r`. | # Caculate R^2 score | _____no_output_____ | MIT | module3-ridge-regression/LS_DS_213_assignment.ipynb | ppmorgoun/DS-Unit-2-Linear-Models |
Simple ARIMAX This code template is for Time Series Analysis and Forecasting to make scientific predictions based on historical time stamped data with the help of ARIMAX algorithm Required Packages | import warnings
import numpy as np
import pandas as pd
import seaborn as se
import matplotlib.pyplot as plt
from statsmodels.tsa.stattools import adfuller
from statsmodels.graphics.tsaplots import plot_acf, plot_pacf
from statsmodels.tsa.statespace.sarimax import SARIMAX
from sklearn.metrics import mean_abs... | _____no_output_____ | Apache-2.0 | Time Series Analysis/Time Series Forecasting/ARIMAX.ipynb | shreepad-nade/ds-seed |
InitializationFilepath of CSV file | file_path = "" | _____no_output_____ | Apache-2.0 | Time Series Analysis/Time Series Forecasting/ARIMAX.ipynb | shreepad-nade/ds-seed |
Variable containing the date time column name of the Time Series data | date = "" | _____no_output_____ | Apache-2.0 | Time Series Analysis/Time Series Forecasting/ARIMAX.ipynb | shreepad-nade/ds-seed |
Target feature for prediction. | target = "" | _____no_output_____ | Apache-2.0 | Time Series Analysis/Time Series Forecasting/ARIMAX.ipynb | shreepad-nade/ds-seed |
Data FetchingPandas is an open-source, BSD-licensed library providing high-performance, easy-to-use data manipulation and data analysis tools.We will use panda's library to read the CSV file using its storage path.And we use the head function to display the initial row or entry. | df = pd.read_csv(file_path)
df.head() | _____no_output_____ | Apache-2.0 | Time Series Analysis/Time Series Forecasting/ARIMAX.ipynb | shreepad-nade/ds-seed |
Data PreprocessingSince the majority of the machine learning models for Time Series Forecasting doesn't handle string category data and Null value, we have to explicitly remove or replace null values. The below snippets have functions, which removes the rows containing null value if any exists. And convert the string ... | def data_preprocess(df, target, date):
df = df.dropna(axis=0, how = 'any')
df[date] = pd.to_datetime(df[date])
df = df.set_index(date)
return df
df = data_preprocess(df,target,date)
df.head()
df.plot(figsize = (15,8))
plt.show() | _____no_output_____ | Apache-2.0 | Time Series Analysis/Time Series Forecasting/ARIMAX.ipynb | shreepad-nade/ds-seed |
Seasonality decomposition Since Simple ARIMAX for non-seasonal data, we need to check for any seasonality in our time series and decompose it.We use the Dickey Fuller Test for testing the seasonality and if the ADF Statistic value is positive, it means that the data has seasonality. Dickey Fuller TestThe Dickey Fuller... | def dickeyFuller(df,target):
# Applying Dickey Fuller Test
X = df.values
result = adfuller(X)
print('ADF Statistic: %f' % result[0])
print('p-value: %f' % result[1])
print('Number of lags used: %d' % result[2])
print('Critical Values:')
for key, value in result[4].items():
... | _____no_output_____ | Apache-2.0 | Time Series Analysis/Time Series Forecasting/ARIMAX.ipynb | shreepad-nade/ds-seed |
To remove the seasonality we use the rolling mean technique for smoothing our data and decomposing any seasonality.This method provides rolling windows over the data. On the resulting windows, we can perform calculations using a statistical function (in this case the mean) in order to decompose the seasonality.For more... | df = dickeyFuller(df,target) | ADF Statistic: -2.256990
p-value: 0.186215
Number of lags used: 15
Critical Values:
1%: -3.448
5%: -2.869
10%: -2.571
| Apache-2.0 | Time Series Analysis/Time Series Forecasting/ARIMAX.ipynb | shreepad-nade/ds-seed |
Autocorrelation PlotWe can calculate the correlation for time series observations with observations with previous time steps, called lags. Because the correlation of the time series observations is calculated with values of the same series at previous times, this is called a serial correlation, or an autocorrelation.A... | x = plot_acf(df, lags=40)
x.set_size_inches(15, 10, forward=True)
plt.show() | _____no_output_____ | Apache-2.0 | Time Series Analysis/Time Series Forecasting/ARIMAX.ipynb | shreepad-nade/ds-seed |
Partial Autocorrelation PlotA partial autocorrelation is a summary of the relationship between an observation in a time series with observations at prior time steps with the relationships of intervening observations removed.The partial autocorrelation at lag k is the correlation that results after removing the effect ... | y = plot_pacf(df, lags=40)
y.set_size_inches(15, 10, forward=True)
plt.show() | _____no_output_____ | Apache-2.0 | Time Series Analysis/Time Series Forecasting/ARIMAX.ipynb | shreepad-nade/ds-seed |
Data SplittingSince we are using a univariate dataset, we can directly split our data into training and testing subsets. The first subset is utilized to fit/train the model. The second subset is used for prediction. The main motive is to estimate the performance of the model on new data. | size = int(len(df)*0.9)
df_train, df_test = df.iloc[:size], df.iloc[size:] | _____no_output_____ | Apache-2.0 | Time Series Analysis/Time Series Forecasting/ARIMAX.ipynb | shreepad-nade/ds-seed |
ModelThe ARIMAX model is an extended version of the ARIMA model. It includes also other independent (predictor) variables. The model is also referred to as the vector ARIMA or the dynamic regression model.The ARIMAX model is similar to a multivariate regression model, but allows to take advantage of autocorrelation th... | model=SARIMAX(df[target],order=(1, 0, 0),seasonal_order=(0,0,0,0))
result=model.fit() | _____no_output_____ | Apache-2.0 | Time Series Analysis/Time Series Forecasting/ARIMAX.ipynb | shreepad-nade/ds-seed |
Model SummaryAfter fitting the training data into our ARIMAX and training it, we can take a look at a brief summary of our model by using the **summary()** function. The followings aspects are included in our model summary:1. Basic Model Details: The first column of our summary table contains the basic details regardi... | result.summary() | _____no_output_____ | Apache-2.0 | Time Series Analysis/Time Series Forecasting/ARIMAX.ipynb | shreepad-nade/ds-seed |
Simple Forecasting | df_train.tail() | _____no_output_____ | Apache-2.0 | Time Series Analysis/Time Series Forecasting/ARIMAX.ipynb | shreepad-nade/ds-seed |
PredictionsBy specifying the start and end time for our predictions, we can easily predict the future points in our time series with the help of our model. | d = df.drop([target], axis = 1)
start_date = d.iloc[size].name
end_date = d.iloc[len(df)-1].name
df_pred = result.predict(start = start_date, end = end_date)
df_pred.head() | _____no_output_____ | Apache-2.0 | Time Series Analysis/Time Series Forecasting/ARIMAX.ipynb | shreepad-nade/ds-seed |
Model AccuracyWe will use the three most popular metrics for model evaluation: Mean absolute error (MAE), Mean squared error (MSE), or Root mean squared error (RMSE). | test = df_test[target]
print("Mean Absolute Error {:.2f}".format(mean_absolute_error(test,df_pred)))
print("Mean Squared Error {:.2f}".format(mean_squared_error(test,df_pred)))
print("Root Mean Squared Error {:.2f}".format(np.sqrt(mean_squared_error(test,df_pred)))) | Mean Absolute Error 8.61
Mean Squared Error 98.90
Root Mean Squared Error 9.94
| Apache-2.0 | Time Series Analysis/Time Series Forecasting/ARIMAX.ipynb | shreepad-nade/ds-seed |
Predictions PlotFirst we make use of plot to plot the predicted values returned by our model based on the test data.After that we plot the actual test data to compare our predictions. | plt.figure(figsize=(18,5))
plt.plot(df_pred[start_date:end_date], color = "red")
plt.plot(df_test, color = "blue")
plt.title("Predictions vs Actual", size = 24)
plt.plot(fontsize="x-large")
plt.show() | _____no_output_____ | Apache-2.0 | Time Series Analysis/Time Series Forecasting/ARIMAX.ipynb | shreepad-nade/ds-seed |
STARmap processing exampleThis notebook demonstrates the processing of STARmap data using starfish. Thedata we present here is a subset of the data used in this[publication](https://doi.org/10.1126/science.aat5691) and was generously provided to us by the authors. | from pprint import pprint
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import starfish
import starfish.data
from starfish.types import Axes
from starfish.util.plot import (
diagnose_registration, imshow_plane, intensity_histogram
)
matplotlib.rcParams["figure.dpi"] = 150 | _____no_output_____ | MIT | notebooks/STARmap.ipynb | kne42/starfish |
Visualize raw dataIn this starmap experiment, starfish exposes a test dataset containing asingle field of view. This dataset contains 672 images spanning 6 rounds`(r)`, 4 channels `(ch)`, and 28 z-planes `(z)`. Each imageis `1024x1024 (y, x)`To examine this data, the vignette displays the max projection of channels an... | experiment = starfish.data.STARmap(use_test_data=True)
stack = experiment['fov_000'].get_image('primary')
ch_r_projection = stack.max_proj(Axes.CH, Axes.ROUND)
f = plt.figure(dpi=150)
imshow_plane(ch_r_projection, sel={Axes.ZPLANE: 15}) | _____no_output_____ | MIT | notebooks/STARmap.ipynb | kne42/starfish |
Visualize the codebook----------------------The STARmap codebook maps pixel intensities across the rounds and channels tothe corresponding barcodes and genes that those pixels code for. For thisdataset, the codebook specifies 160 gene targets. | print(experiment.codebook) | _____no_output_____ | MIT | notebooks/STARmap.ipynb | kne42/starfish |
RegistrationStarfish exposes some simple tooling to identify registration shifts.`starfish.util.plot.diagnose_registration` takes an ImageStack and aset of selectors, each of which maps `Axes` objectsto indices that specify a particular 2d image.Below the vignette projects the channels and z-planes and examines thereg... | projection = stack.max_proj(Axes.CH, Axes.ZPLANE)
reference_image = projection.sel({Axes.ROUND: 1})
ltt = starfish.image.LearnTransform.Translation(
reference_stack=reference_image,
axes=Axes.ROUND,
upsampling=1000,
)
transforms = ltt.run(projection) | _____no_output_____ | MIT | notebooks/STARmap.ipynb | kne42/starfish |
How big are the identified translations? | pprint([t[2].translation for t in transforms.transforms]) | _____no_output_____ | MIT | notebooks/STARmap.ipynb | kne42/starfish |
Apply the translations | warp = starfish.image.ApplyTransform.Warp()
stack = warp.run(
stack=stack,
transforms_list=transforms,
) | _____no_output_____ | MIT | notebooks/STARmap.ipynb | kne42/starfish |
Show the effect of registration. | post_projection = stack.max_proj(Axes.CH, Axes.ZPLANE)
f, (ax1, ax2) = plt.subplots(ncols=2)
sel_0 = {Axes.ROUND: 0, Axes.X: (500, 600), Axes.Y: (500, 600)}
sel_1 = {Axes.ROUND: 1, Axes.X: (500, 600), Axes.Y: (500, 600)}
diagnose_registration(
projection, sel_0, sel_1, ax=ax1, title='pre-registered'
)
diagnose_regi... | _____no_output_____ | MIT | notebooks/STARmap.ipynb | kne42/starfish |
The plot shows that the slight offset has been corrected.Equalize channel intensities----------------------------The second stage of the STARmap pipeline is to align the intensitydistributions across channels and rounds. Here we calculate a referencedistribution by sorting each image's intensities in increasing order a... | mh = starfish.image.Filter.MatchHistograms({Axes.CH, Axes.ROUND})
scaled = mh.run(stack, in_place=False, verbose=True, n_processes=8)
def plot_scaling_result(
template: starfish.ImageStack, scaled: starfish.ImageStack
):
f, (before, after) = plt.subplots(ncols=4, nrows=2)
for channel, ax in enumerate(before... | _____no_output_____ | MIT | notebooks/STARmap.ipynb | kne42/starfish |
Find spots----------Finally, a local blob detector that finds spots in each (z, y, x) volumeseparately is applied. The user selects an "anchor round" and spots found inall channels of that round are used to seed a local search across other roundsand channels. The closest spot is selected, and any spots outside the sear... | lsbd = starfish.spots.DetectSpots.LocalSearchBlobDetector(
min_sigma=1,
max_sigma=8,
num_sigma=10,
threshold=np.percentile(np.ravel(stack.xarray.values), 95),
exclude_border=2,
anchor_round=0,
search_radius=10,
)
intensities = lsbd.run(scaled, n_processes=8) | _____no_output_____ | MIT | notebooks/STARmap.ipynb | kne42/starfish |
Decode spots------------Next, spots are decoded. There is really no good way to display 3-d spotdetection in 2-d planes, so we encourage you to grab this notebook anduncomment the below lines. | decoded = experiment.codebook.decode_per_round_max(intensities.fillna(0))
decode_mask = decoded['target'] != 'nan'
# %gui qt
# viewer = starfish.display(
# stack, decoded[decode_mask], radius_multiplier=2, mask_intensities=0.1
# ) | _____no_output_____ | MIT | notebooks/STARmap.ipynb | kne42/starfish |
Using the Object-Oriented Interface | import coronagraph as cg
print(cg.__version__)
noise = cg.CoronagraphNoise(THERMAL=True)
lamhr, Ahr, fstar = cg.get_earth_reflect_spectrum()
noise.run_count_rates(Ahr, lamhr, fstar)
noise.make_fake_data(texp = 10)
fig, ax = noise.plot_spectrum()
fig, ax = noise.plot_SNR()
fig, ax = noise.plot_time_to_wantsnr() | _____no_output_____ | MIT | docs/notebooks/interface_example.ipynb | jlustigy/coronagraph |
Plotting with matplotlib Setup | %matplotlib inline
import numpy as np
import pandas as pd
pd.set_option('display.max_columns', 10)
pd.set_option('display.max_rows', 10) | _____no_output_____ | MIT | pandas/Demos/plotting.ipynb | WebucatorTraining/classfiles-actionable-python |
Getting the pop2019 DataFrame | csv ='../csvs/nc-est2019-agesex-res.csv'
pops = pd.read_csv(csv, usecols=['SEX', 'AGE', 'POPESTIMATE2019'])
def fix_sex(sex):
if sex == 0:
return 'T'
elif sex == 1:
return 'M'
else: # 2
return 'F'
pops.SEX = pops.SEX.apply(fix_sex)
pops = pops.pivot(index='AGE', columns='SEX',... | _____no_output_____ | MIT | pandas/Demos/plotting.ipynb | WebucatorTraining/classfiles-actionable-python |
Create a Line Plot | # Create the plot.
plt_pop = pops.plot(
title = "Population by Age: 2019",
style=['b--', 'm^', 'k-'],
figsize=(12, 6),
lw=2
)
# Include gridlines.
plt_pop.grid(True)
# Set the x and y labels.
plt_pop.set_xlabel('Age')
plt_pop.set_ylabel('Population')
# Create the legend.
plt_pop.legend(['M', 'F', 'A'... | _____no_output_____ | MIT | pandas/Demos/plotting.ipynb | WebucatorTraining/classfiles-actionable-python |
Create a Bar Plot | csv ='../csvs/mantle.csv'
mantle = pd.read_csv(csv, index_col='Year',
usecols=['Year', '2B', '3B', 'HR'])
mantle
# Create the plot.
plt_mantle = mantle.plot(
kind='bar',
title = 'Mickey Mantle: Doubles, Triples, and Home Runs',
figsize=(12, 6),
width=.8,
fontsize=16
)
# Include... | _____no_output_____ | MIT | pandas/Demos/plotting.ipynb | WebucatorTraining/classfiles-actionable-python |
Activation function is an identity function. It is trivial. Many activation functions. Out of them, these are famous -1. Sigmoid - range: [0, 1]2. Tanh - range: [-1, 1] - zero centered, prefered in hidden layers over (1)3. ReLUDue to vanishing gradient problem, Sigmoid and Tanh functions are being avoided. But to unde... | import numpy as np
def sigmoid(z):
return 1 / (1 + np.exp(-z))
sigmoid(-2) | _____no_output_____ | MIT | Activation_Functions.ipynb | hemanthsunny/machine_learning |
Tanh | import numpy as np
def tanh(z):
return np.tanh(z)
def tanh2(z):
return (np.exp(z) - np.exp(-z)) / (np.exp(z) + np.exp(-z))
print("Tanh :", tanh(-2))
print("Tanh2 :", tanh2(-2)) | Tanh : -0.9640275800758169
Tanh2 : -0.964027580075817
| MIT | Activation_Functions.ipynb | hemanthsunny/machine_learning |
ReLU | import numpy as np
def relu(z):
return z*(z>0)
relu(10) | _____no_output_____ | MIT | Activation_Functions.ipynb | hemanthsunny/machine_learning |
Leaky ReLU | import numpy as np
def leaky_relu(z):
return np.maximum(0.01 * z, z)
leaky_relu(-2)
| _____no_output_____ | MIT | Activation_Functions.ipynb | hemanthsunny/machine_learning |
import os
import glob
import cv2
import numpy as np
from sklearn.model_selection import train_test_split
from re import search
from tensorflow import keras
#-----------------------load images---------------------------------------------
#imageDir=os.path.abspath('/content/drive/MyDrive/Colab Notebooks/ImageClassificati... | 4/4 - 21s - loss: 166.1185 - accuracy: 0.3280 - 21s/epoch - 5s/step
Restored model, accuracy: 32.80%
| MIT | CNNBreatCancer.ipynb | RihaChri/ImageClassificationBreastCancer | |
We notice that pdays is having [25, 50, 75] percentiles as -1 which indicates that missing values are denoted by -1. | data[data.duplicated()] | _____no_output_____ | MIT | BI_Capstone_Project1/Solution.ipynb | amazerahul/BI_Capstone_Project |
Which age was targeted most and which one of them responded positively? | plt.figure(figsize=(13,5))
plt.subplot(1, 2, 1)
sns.distplot(data['age']).set_title('Age distribution')
plt.subplot(1, 2, 2)
sns.violinplot(x = 'response', y = 'age', data=data).set_title('Relationship between age and response') | _____no_output_____ | MIT | BI_Capstone_Project1/Solution.ipynb | amazerahul/BI_Capstone_Project |
* We see from the above graph that most people targeted in the campaign lie in the age group of 25 to 50.* It is clear that people lying in the age group of 25 to 40 respond positively than any other age group. Does job description affect the chances of success? | grouped_job = pd.DataFrame(data.groupby(['job'])['response'].value_counts(normalize=True))
grouped_job.rename(columns={"response" : "pct"}, inplace=True)
grouped_job.reset_index(inplace=True)
plt.figure(figsize=(15,5))
sns.barplot(x='job', y='pct', hue='response', data=grouped_job).set_title('Response by job descriptio... | _____no_output_____ | MIT | BI_Capstone_Project1/Solution.ipynb | amazerahul/BI_Capstone_Project |
* From the above plot it can be said that customers responding positively to the camapign are very less.* Customer with job role as management seems to interested more than any other job role. Does people with high profile job and high salary have greater chances of positive response? | job_salary = pd.DataFrame(data.groupby('job')['salary'].unique().apply(lambda x : x[0])).reset_index()
job_response = pd.crosstab(data['job'], data['response']).reset_index()
data1 = pd.merge(job_salary, job_response)
data1.sort_values('yes', ascending=False)
plt.figure(figsize=(15,5))
plt.subplot(1, 2, 1)
g = sns.barp... | _____no_output_____ | MIT | BI_Capstone_Project1/Solution.ipynb | amazerahul/BI_Capstone_Project |
Yes, it is clearly visible that people with high job profile and salary tend to be more interested. Does marital status decide the response type? | np.log(pd.crosstab(data['marital'], data['response'])).plot.bar(title = 'Response based on marital status') | _____no_output_____ | MIT | BI_Capstone_Project1/Solution.ipynb | amazerahul/BI_Capstone_Project |
Positive response is almost neutral in case of marital status. Does married people of younger age responds positively to the campaign? | data2 = data[(data['marital']=='married') & (data['age']<25)]
sns.barplot(x='response', y='age', data=data2) | _____no_output_____ | MIT | BI_Capstone_Project1/Solution.ipynb | amazerahul/BI_Capstone_Project |
Ratio of yes/no among married young customer seems to be equal. Age distribution based on marital status | m = sns.FacetGrid(data, col='marital', height = 4)
m.map(plt.hist, 'age') | _____no_output_____ | MIT | BI_Capstone_Project1/Solution.ipynb | amazerahul/BI_Capstone_Project |
* From the 1st plot it can be seen that mostly married people lie around 25 to 56.* Single people lie in the age group of 25 to 37.* Most probably divorced people lie in the age group of 30 to 60. What is the relationship between education level and salary? | sns.boxplot(x='education', y='salary', data=data).set_title('Boxplot distribution of salary based on education level')
plt.show() | _____no_output_____ | MIT | BI_Capstone_Project1/Solution.ipynb | amazerahul/BI_Capstone_Project |
It is clear from the above graph that people with high level of education tend to have high salary. Are customer of low job profile and married are most responsive one? | grouped_job_married = pd.DataFrame(data[data['marital']=='married'].groupby(['job'])['response'].value_counts(normalize=True))
grouped_job_married.rename(columns={"response" : "pct"}, inplace=True)
grouped_job_married.reset_index(inplace=True)
plt.figure(figsize=(15,5))
sns.barplot(x='job', y='pct', hue='response', dat... | _____no_output_____ | MIT | BI_Capstone_Project1/Solution.ipynb | amazerahul/BI_Capstone_Project |
Yes, people who are married and have low job profile tend to response more positvely than any other job profile. Are the targeted customers interested in such campaigns? | data.head() | _____no_output_____ | MIT | BI_Capstone_Project1/Solution.ipynb | amazerahul/BI_Capstone_Project |
In order to answer this questions checking of previous campaign records would be appropriate. | grouped_poutcome = pd.DataFrame(data['poutcome'].value_counts(normalize=True))
grouped_poutcome = grouped_poutcome.reset_index().rename(columns={"index" : "poutcome", "poutcome" : "pct"})
sns.barplot(x='poutcome', y='pct', data=grouped_poutcome).set_title('Results of previous campaign')
plt.figure(figsize=(12, 4))
plt.... | _____no_output_____ | MIT | BI_Capstone_Project1/Solution.ipynb | amazerahul/BI_Capstone_Project |
It seems that mostly people were contacted within few days still people dosen't seems to be interested in such campaigns. Does targeted customer with high job profile and high salary interested in such campaigns anymore? | data.groupby('job')['salary'].unique().apply(lambda x : x[0]).sort_values(ascending=False)
data3 = data[(data['salary']>50000) & (data['targeted']=='yes')]['response'].value_counts(normalize=True)
data3 = pd.DataFrame(data3)
data3 = data3.reset_index().rename(columns={'index' : 'response', 'response' : 'pct'})
grouped_... | _____no_output_____ | MIT | BI_Capstone_Project1/Solution.ipynb | amazerahul/BI_Capstone_Project |
There is not much significant difference in the response of high job profile customers from previous to current campaign. Distribution of diff in salary and balance. | data['expenditure'] = data['salary'] - data['balance']
plt.figure(figsize=(15,5))
sns.distplot(data['expenditure'], bins=10).set_title('Distribution of expenditure') | _____no_output_____ | MIT | BI_Capstone_Project1/Solution.ipynb | amazerahul/BI_Capstone_Project |
It seems there are people with varying limit in terms of expenditure. What is the response of customer with high expenditure? | data['expenditure'].plot.box()
threshold = data['expenditure'].quantile(0.90)
less_exp = pd.DataFrame(data[data['expenditure']<threshold]['response'].value_counts(normalize=True))
less_exp = less_exp.reset_index().rename(columns={'index' : 'response', 'response' : 'pct'})
high_exp = pd.DataFrame(data[data['expenditure... | _____no_output_____ | MIT | BI_Capstone_Project1/Solution.ipynb | amazerahul/BI_Capstone_Project |
Although, the response from customers are very few. But, those who spend less have greater chances of postive response as compared to those who spend more. Is there any relationship between education level and bank balance? | sns.boxplot(x='education', y='balance', data=data).set_title('Boxplot distribution of balance by education level') | _____no_output_____ | MIT | BI_Capstone_Project1/Solution.ipynb | amazerahul/BI_Capstone_Project |
There is not much difference in the statistical measure of balance by education. Rather, it depends on person-to-person. What are the chances of customer responding positively who already have home and personal loan? | data[(data['housing']=='yes') & (data['loan']=='yes')]['response'].value_counts(normalize=True).plot.bar(title='Customer response having home and personal loan') | _____no_output_____ | MIT | BI_Capstone_Project1/Solution.ipynb | amazerahul/BI_Capstone_Project |
Not much people are interested in such campaigns who already have loans. What are the ways of contacting a customer. And which contact types are most effective? | grouped_contact = pd.DataFrame(data['contact'].value_counts(normalize=True)).reset_index().rename(columns={'index' : 'contact_type', 'contact' : 'pct'})
sns.barplot(x='contact_type', y ='pct', data=grouped_contact).set_title('Different contact types for contacting customers')
grouped_contact_response = pd.DataFrame(dat... | _____no_output_____ | MIT | BI_Capstone_Project1/Solution.ipynb | amazerahul/BI_Capstone_Project |
Most of the people use cell phones as a communication device and it would be better if we target customer who use cell phones. In which month and day most customer were addressed? | grouped_month = pd.DataFrame(data['month'].value_counts(normalize=True)).reset_index().rename(columns={"index" : "month", "month" : "pct"})
grouped_day = pd.DataFrame(data['day'].value_counts(normalize=True)).reset_index().rename(columns={"index" : "day", "day" : "pct"})
plt.figure(figsize=(15, 5))
plt.subplot(1, 2, 1)... | _____no_output_____ | MIT | BI_Capstone_Project1/Solution.ipynb | amazerahul/BI_Capstone_Project |
It seems in the month of may most customers are handled and this may be due to the fact that vacations are going on at that point of time. What is the effect of regular campaigning to a customer? | grouped_camp = pd.DataFrame(data['campaign'].value_counts(normalize=True)).reset_index().rename(columns={"index" : "campaign_freq", "campaign" : "pct"})
plt.figure(figsize=(15,5))
plt.subplot(1, 2, 1)
sns.barplot(x='campaign_freq', y='pct', data=grouped_camp).set_title('No of times a customer was contacted during a cam... | _____no_output_____ | MIT | BI_Capstone_Project1/Solution.ipynb | amazerahul/BI_Capstone_Project |
* It is clear from above graph that as people are being contacted more and more for a particular contact they seems to lose interest.* The campaign team also needs to keep track of these records as it will save their time and focus on things which will add value to them. Modelling | def get_dummies(x,df):
temp = pd.get_dummies(df[x], prefix = x, prefix_sep = '_', drop_first = True)
df = pd.concat([df, temp], axis = 1)
df.drop([x], axis = 1, inplace = True)
return df
X = data.drop(['response', 'campaign_size'], axis=1)
y = data.response
X.loc[X['pdays']==-1, 'pdays'] = 10000
# Crea... | _____no_output_____ | MIT | BI_Capstone_Project1/Solution.ipynb | amazerahul/BI_Capstone_Project |
Random Forest Classifier | rfc = RandomForestClassifier()
random_search = RandomizedSearchCV(rfc, param_distributions=params_grid, n_iter=10, cv=10)
random_search.fit(X_train, y_train)
random_search.best_estimator_.n_estimators
random_search.best_estimator_.criterion
random_search.best_estimator_.max_depth
rfc2 = RandomForestClassifier(n_estimat... | precision recall f1-score support
0 0.92 0.97 0.95 7987
1 0.64 0.35 0.45 1056
accuracy 0.90 9043
macro avg 0.78 0.66 0.70 9043
weighted avg 0.89 0.90 0.89 ... | MIT | BI_Capstone_Project1/Solution.ipynb | amazerahul/BI_Capstone_Project |
This will define a class ```FitnessMax``` which inherits the Fitness class of deep.base module. The attribute weight which is a tuple is used to specify whether fitness function is to be maximized (weights=1.0) or minimized weights=-1.0. The DEAP library allows multi-objective Fitness function. IndividualNext we creat... | # Now we create a individual class
creator.create("Individual", list, fitness=creator.FitnessMax) | _____no_output_____ | MIT | Chapter05/GuessTheWord.ipynb | PacktPublishing/Hands-On-Artificial-Intelligence-for-IoT |
PopulationOnce the individuals are created we need to create population and define gene pool, to do this we use DEAP toolbox. All the objects that we will need now onwards- an individual, the population, the functions, the operators and the arguments are stored in the container called ```Toolbox```We can add or remove... | toolbox = base.Toolbox()
# Gene Pool
toolbox.register("attr_string", random.choice, string.ascii_letters + string.digits )
#Number of characters in word
word = list('hello')
N = len(word)
# Initialize population
toolbox.register("individual", tools.initRepeat, creator.Individual, toolbox.attr_string, N )
toolbox.regi... | _____no_output_____ | MIT | Chapter05/GuessTheWord.ipynb | PacktPublishing/Hands-On-Artificial-Intelligence-for-IoT |
We define the other operators/functions we will need by registering them in the toolbox. This allows us to easily switch between the operators if desired. Evolving the PopulationOnce the representation and the genetic operators are chosen, we will define an algorithm combining all the individual parts and performing th... | def main():
random.seed(64)
# create an initial population of 300 individuals (where
# each individual is a list of integers)
pop = toolbox.population(n=300)
# CXPB is the probability with which two individuals
# are crossed
#
# MUTPB is the probability for mutating an individua... | Start of evolution
Evaluated 300 individuals
-- Generation 1 --
Evaluated 178 individuals
Min 0.0
Max 2.0
Avg 0.22
Std 0.4526956299030656
-- Generation 2 --
Evaluated 174 individuals
Min 0.0
Max 2.0
Avg 0.51
Std 0.613650280425803
-- Generation 3 --
Evaluated 191 individuals
Min 0.0
Max 3.0
... | MIT | Chapter05/GuessTheWord.ipynb | PacktPublishing/Hands-On-Artificial-Intelligence-for-IoT |
Variance | x = torch.randn(10)
x
plt.scatter(range(x.numel()),x)
mean = x.mean()
mean
x - mean
wrong_variance = (x-mean).mean()
wrong_variance, wrong_variance.round() | _____no_output_____ | MIT | Colab Notebooks/learning/20191012_important_maths_formulas_in_pytorch.ipynb | ankschoubey/notes |
The reason that this is 0 is because positive and negatives cancel out. Therefore we need to make sure everything is positive | # mean absolute deviation
mad = (x-mean).abs().mean()
mad
(x-mean).abs()
# standard deviation
sd = (x-mean).pow(2).mean().sqrt()
sd
sd
| _____no_output_____ | MIT | Colab Notebooks/learning/20191012_important_maths_formulas_in_pytorch.ipynb | ankschoubey/notes |
from google.colab import drive
drive.mount('/content/drive')
!nvidia-smi
!pip install transformers
!pip install -q -U watermark
%reload_ext watermark
%watermark -v -p numpy,pandas,torch,transformers
!pip install sklearn
import sklearn | Requirement already satisfied: sklearn in /usr/local/lib/python3.6/dist-packages (0.0)
Requirement already satisfied: scikit-learn in /usr/local/lib/python3.6/dist-packages (from sklearn) (0.22.2.post1)
Requirement already satisfied: scipy>=0.17.0 in /usr/local/lib/python3.6/dist-packages (from scikit-learn->sklearn) (... | MIT | Sentiment_Analysis_Series_part_2(4000samplesonsent140).ipynb | kumarikumari/Keras-Deep-Learning-Cookbook | |
Making the necessary imports | import transformers
from transformers import XLNetTokenizer, XLNetModel, AdamW, get_linear_schedule_with_warmup
import torch
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from matplotlib import rc
from sklearn.model_selection import train_test_split
from sklearn.metrics i... | _____no_output_____ | MIT | Sentiment_Analysis_Series_part_2(4000samplesonsent140).ipynb | kumarikumari/Keras-Deep-Learning-Cookbook |
Data Preprocessing | from google.colab import files
files.upload()
df = pd.read_csv('sent140_preprocessed.csv')
df.head()
from sklearn.utils import shuffle
df = shuffle(df)
df.head(20)
df = df[:4000]
len(df)
import re
def clean_text(text1):
text = re.sub(r"@[A-Za-z0-9]+", ' ', text)
text = re.sub(r"https?://[A-Za-z0-9./]+", ' ', te... | _____no_output_____ | MIT | Sentiment_Analysis_Series_part_2(4000samplesonsent140).ipynb | kumarikumari/Keras-Deep-Learning-Cookbook |
Playing with XLNetTokenizer | from transformers import XLNetTokenizer, XLNetModel
PRE_TRAINED_MODEL_NAME = 'xlnet-base-cased'
tokenizer = XLNetTokenizer.from_pretrained(PRE_TRAINED_MODEL_NAME)
input_txt = "India is my country. All Indians are my brothers and sisters"
encodings = tokenizer.encode_plus(input_txt, add_special_tokens=True, max_length=1... | _____no_output_____ | MIT | Sentiment_Analysis_Series_part_2(4000samplesonsent140).ipynb | kumarikumari/Keras-Deep-Learning-Cookbook |
Checking the distribution of token lengths | token_lens = []
for txt in df['text']:
tokens = tokenizer.encode(txt, max_length=512)
token_lens.append(len(tokens))
sns.distplot(token_lens)
plt.xlim([0, 1024]);
plt.xlabel('Token count');
MAX_LEN = 512 | _____no_output_____ | MIT | Sentiment_Analysis_Series_part_2(4000samplesonsent140).ipynb | kumarikumari/Keras-Deep-Learning-Cookbook |
Custom Dataset class | class sent140(Dataset):
def __init__(self, reviews, targets, tokenizer, max_len):
self.reviews = reviews
self.targets = targets
self.tokenizer = tokenizer
self.max_len = max_len
def __len__(self):
return len(self.reviews)
def __getitem__(self, item):
... | _____no_output_____ | MIT | Sentiment_Analysis_Series_part_2(4000samplesonsent140).ipynb | kumarikumari/Keras-Deep-Learning-Cookbook |
Custom Dataloader | def create_data_loader(df, tokenizer, max_len, batch_size):
ds = sent140(
reviews=df.text.to_numpy(),
targets=df.polarity.to_numpy(),
tokenizer=tokenizer,
max_len=max_len
)
return DataLoader(
ds,
batch_size=batch_size,
num_workers=4
)
BATCH_SIZE = 4
train_data_loader = create_data_... | _____no_output_____ | MIT | Sentiment_Analysis_Series_part_2(4000samplesonsent140).ipynb | kumarikumari/Keras-Deep-Learning-Cookbook |
Loading the Pre-trained XLNet model for sequence classification from huggingface transformers | from transformers import XLNetForSequenceClassification
model = XLNetForSequenceClassification.from_pretrained('xlnet-base-cased', num_labels = 2)
model = model.to(device)
model | _____no_output_____ | MIT | Sentiment_Analysis_Series_part_2(4000samplesonsent140).ipynb | kumarikumari/Keras-Deep-Learning-Cookbook |
Setting Hyperparameters | EPOCHS = 3
param_optimizer = list(model.named_parameters())
no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
optimizer_grouped_parameters = [
{'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)], 'weight_decay': 0.01},
... | _____no_output_____ | MIT | Sentiment_Analysis_Series_part_2(4000samplesonsent140).ipynb | kumarikumari/Keras-Deep-Learning-Cookbook |
Sanity check with one batch | data = next(iter(val_data_loader))
data.keys()
input_ids = data['input_ids'].to(device)
attention_mask = data['attention_mask'].to(device)
targets = data['targets'].to(device)
print(input_ids.reshape(4,512).shape) # batch size x seq length
print(attention_mask.shape) # batch size x seq length
input_ids[0]
outputs = mod... | _____no_output_____ | MIT | Sentiment_Analysis_Series_part_2(4000samplesonsent140).ipynb | kumarikumari/Keras-Deep-Learning-Cookbook |
Defining the training step function | from sklearn import metrics
def train_epoch(model, data_loader, optimizer, device, scheduler, n_examples):
model = model.train()
losses = []
acc = 0
counter = 0
for d in data_loader:
input_ids = d["input_ids"].reshape(4,512).to(device)
attention_mask = d["attention_mask"].to(devic... | _____no_output_____ | MIT | Sentiment_Analysis_Series_part_2(4000samplesonsent140).ipynb | kumarikumari/Keras-Deep-Learning-Cookbook |
Defining the evaluation function | def eval_model(model, data_loader, device, n_examples):
model = model.eval()
losses = []
acc = 0
counter = 0
with torch.no_grad():
for d in data_loader:
input_ids = d["input_ids"].reshape(4,512).to(device)
attention_mask = d["attention_mask"].to(device)
... | _____no_output_____ | MIT | Sentiment_Analysis_Series_part_2(4000samplesonsent140).ipynb | kumarikumari/Keras-Deep-Learning-Cookbook |
Fine-tuning the pre-trained model | %%time
history = defaultdict(list)
best_accuracy = 0
for epoch in range(EPOCHS):
print(f'Epoch {epoch + 1}/{EPOCHS}')
print('-' * 10)
train_acc, train_loss = train_epoch(
model,
train_data_loader,
optimizer,
device,
scheduler,
len(df_train)
)
... | Epoch 1/3
----------
Train loss 0.5852309298217296 Train accuracy 0.7585
Val loss 0.7540176632255315 Val accuracy 0.766
Epoch 2/3
----------
Train loss 0.5752474646037444 Train accuracy 0.764
Val loss 0.6447369921803474 Val accuracy 0.765
Epoch 3/3
----------
Train loss 0.5502564660999923 Train accuracy 0.812
Val los... | MIT | Sentiment_Analysis_Series_part_2(4000samplesonsent140).ipynb | kumarikumari/Keras-Deep-Learning-Cookbook |
Evaluation of the fine-tuned model | model.load_state_dict(torch.load('C:\\Users\\BUJJI\\Desktop\\NLP\\models\\xlnet_model1.bin'))
model = model.to(device)
test_acc, test_loss = eval_model(
model,
test_data_loader,
device,
len(df_test)
)
print('Test Accuracy :', test_acc)
print('Test Loss :', test_loss)
def get_predictions(model, data_loader):
... | precision recall f1-score support
negative 0.83 0.86 0.84 768
positive 0.47 0.42 0.44 232
accuracy 0.76 1000
macro avg 0.65 0.64 0.64 1000
weighted avg 0.75 0.76 0.75 ... | MIT | Sentiment_Analysis_Series_part_2(4000samplesonsent140).ipynb | kumarikumari/Keras-Deep-Learning-Cookbook |
Custom prediction function on raw text | def predict_sentiment(text):
review_text = text
encoded_review = tokenizer.encode_plus(
review_text,
max_length=MAX_LEN,
add_special_tokens=True,
return_token_type_ids=False,
pad_to_max_length=False,
return_attention_mask=True,
return_tensors='pt',
)
input_ids = pad_sequenc... | Positive score: 0.9775038957595825
Negative score: 0.022496072575449944
Review text: This is the best movie I have ever seen!! The story is such a motivation
Sentiment : positive
| MIT | Sentiment_Analysis_Series_part_2(4000samplesonsent140).ipynb | kumarikumari/Keras-Deep-Learning-Cookbook |
06_Business_Insights In this section, we will expend upon the features used by the model and attempt to explain its significance as well as contributions to the pricing model.Accordingly, in Section Four, we identified the following key features that that are strong predictors of housing price based upon a combination... | # model coefficients
prod_model_rfe | _____no_output_____ | MIT | project_2/06_Production_Model_Insights.ipynb | bearylogical/ga-test |
Import Libraries | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import matplotlib.ticker as ticker
%matplotlib inline
rfe_columns = ['Total Bsmt SF', 'Gr Liv Area', 'age_since_built', 'house_exter_score',
'Land Contour_HLS', 'Land Contour_Low', 'Land Contour_Lvl'] | _____no_output_____ | MIT | project_2/06_Production_Model_Insights.ipynb | bearylogical/ga-test |
Load in Training Set for Exploration | train = pd.read_csv('./datasets/imputed_train.csv')
new_train = train[(train['Total Bsmt SF']<4000) & (train['Total Bsmt SF']>0)]
plt.figure(figsize=(15,10))
ax = sns.scatterplot(x='Total Bsmt SF',y='SalePrice',data=new_train,hue='Total Bsmt SF')
ax.set_title("Total Basement Area against Sale Prices", fontname='Helveti... | _____no_output_____ | MIT | project_2/06_Production_Model_Insights.ipynb | bearylogical/ga-test |
Hyperparameter Tuning and Grid Search CV Read data from pickle file | import pickle as pkl
with open('../data/titanic_tansformed.pkl', 'rb') as f:
df_data = pkl.load(f)
df_data.head()
data = df_data.drop("Survived",axis=1)
label = df_data["Survived"] | _____no_output_____ | MIT | classification/notebooks/06 - Hyperparameter and Grid Search CV.ipynb | pshn111/Machine-Learning-Package |
Divide into train and test split | from sklearn.model_selection import train_test_split
data_train, data_test, label_train, label_test = train_test_split(data, label, test_size = 0.2, random_state = 101)
from sklearn.model_selection import StratifiedKFold
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import cross_val_s... | Best Score 0.8045007032348804
Best Value of C 0.5
Best penalty l1
| MIT | classification/notebooks/06 - Hyperparameter and Grid Search CV.ipynb | pshn111/Machine-Learning-Package |
Optimal Model after a GridCV Search | optimal_model = LogisticRegression(C=log_grid.best_estimator_.C, penalty=log_grid.best_estimator_.penalty)
optimal_model.fit(data_train, label_train)
predictions = optimal_model.predict(data_test)
print('Accuracy', optimal_model.score(data_test, label_test))
print('Coefficients', optimal_model.coef_)
print('Intercept'... | Accuracy 0.8146067415730337
Coefficients [[-2.88708422e-02 -2.72763004e-01 -1.09620072e-01 2.39262062e-03
7.90405016e-01 0.00000000e+00 -1.08574363e+00 2.56316901e+00
0.00000000e+00 0.00000000e+00 0.00000000e+00 -3.09675215e-01]]
Intercept [0.]
| MIT | classification/notebooks/06 - Hyperparameter and Grid Search CV.ipynb | pshn111/Machine-Learning-Package |
Bulk RNA-seq eQTL analysisThis notebook provide a command generator on the XQTL workflow so it can automate the work for data preprocessing and association testing on multiple data collection as proposed. | %preview ../images/eqtl_command.png | _____no_output_____ | MIT | code/commands_generator/eQTL_analysis_commands.ipynb | cumc/xqtl-pipeline |
This master control notebook is mainly to serve the 8 tissues snuc_bulk_expression analysis, but should be functional on all analysis where expression data are are a tsv table in a bed.gz like format.Input: A recipe file,each row is a data collection and with the following column: Theme name of... | pd.DataFrame({"Theme":"MWE","molecular_pheno":"MWE.log2cpm.tsv","genotype_file":"MWE.bed","covariate_file":"MWE.covariate.cov.gz"}).to_csv("/mnt/vast/hpc/csg/snuc_pseudo_bulk/eight_tissue_analysis/MWE/command_generator",sep = "\t",index = 0) | _____no_output_____ | MIT | code/commands_generator/eQTL_analysis_commands.ipynb | cumc/xqtl-pipeline |
| Theme | molecular_pheno | genotype_file |covariate_file|| ----------- | ----------- |-----------||| MWE | MWE.log2cpm.tsv | /data/genotype_data/GRCh38_liftedover_sorted_all.add_chr.leftnorm.filtered.bed |MWE.covariate.cov.gz| Minimal Working Example GenotypeThe MWE for the genotype session can be ra... | sos run pipeline/eQTL_analysis_commands.ipynb plink_per_chrom \
--ref_fasta reference_data/GRCh38_full_analysis_set_plus_decoy_hla.noALT_noHLA_noDecoy_ERCC.fasta \
--genoFile mwe_genotype.vcf.gz \
--dbSNP_vcf reference_data/00-All.vcf.gz \
--sample_participant_lookup reference_data/sampleSheetAft... | _____no_output_____ | MIT | code/commands_generator/eQTL_analysis_commands.ipynb | cumc/xqtl-pipeline |
Per tissue analysisA MWE for the core per tissue analysis can be ran with the following commands, a complete collection of input file as well as intermediate output of the analysis can be found at [here](https://drive.google.com/drive/folders/16ZUsciZHqCeeEWwZQR46Hvh5OtS8lFtA?usp=sharing). | sos run pipeline/eQTL_analysis_commands.ipynb sumstat_merge \
--recipe MWE.recipe \
--genotype_list plink_files_list.txt \
--annotation_gtf reference_data/genes.reformatted.gene.gtf \
--sample_participant_lookup reference_data/sampleSheetAfterQC.txt \
--Association_option "TensorQTL" -n ... | _____no_output_____ | MIT | code/commands_generator/eQTL_analysis_commands.ipynb | cumc/xqtl-pipeline |
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