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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Setting the index- Now we need to set the index of the data-frame so that it contains the sequence of dates. | googl.set_index(pd.to_datetime(googl['Date']), inplace=True)
googl.index[0]
type(googl.index[0]) | _____no_output_____ | CC-BY-4.0 | src/main/ipynb/pandas.ipynb | pperezgr/python-bigdata |
Plotting series- We can plot a series in a dataframe by invoking its `plot()` method.- Here we plot a time-series of the daily traded volume: | ax = googl['Volume'].plot()
plt.show() | _____no_output_____ | CC-BY-4.0 | src/main/ipynb/pandas.ipynb | pperezgr/python-bigdata |
Adjusted closing prices as a time series | googl['Adj Close'].plot()
plt.show() | _____no_output_____ | CC-BY-4.0 | src/main/ipynb/pandas.ipynb | pperezgr/python-bigdata |
Slicing series using date/time stamps- We can slice a time series by specifying a range of dates or times.- Date and time stamps are specified strings representing dates in the required format. | googl['Adj Close']['1-1-2016':'1-1-2017'].plot()
plt.show() | _____no_output_____ | CC-BY-4.0 | src/main/ipynb/pandas.ipynb | pperezgr/python-bigdata |
Resampling - We can *resample* to obtain e.g. weekly or monthly prices.- In the example below the `'W'` denotes weekly.- See [the documentation](http://pandas.pydata.org/pandas-docs/stable/timeseries.htmloffset-aliases) for other frequencies.- We group data into weeks, and then take the last value in each week.- For d... | weekly_prices = googl['Adj Close'].resample('W').last()
weekly_prices.head()
weekly_prices.plot()
plt.title('Prices for GOOGL sampled at weekly frequency')
plt.show() | _____no_output_____ | CC-BY-4.0 | src/main/ipynb/pandas.ipynb | pperezgr/python-bigdata |
Converting prices to log returns | weekly_rets = np.diff(np.log(weekly_prices))
plt.plot(weekly_rets)
plt.xlabel('t'); plt.ylabel('$r_t$')
plt.title('Weekly log-returns for GOOGL')
plt.show() | _____no_output_____ | CC-BY-4.0 | src/main/ipynb/pandas.ipynb | pperezgr/python-bigdata |
Converting the returns to a series- Notice that in the above plot the time axis is missing the dates.- This is because the `np.diff()` function returns an array instead of a data-frame. | type(weekly_rets) | _____no_output_____ | CC-BY-4.0 | src/main/ipynb/pandas.ipynb | pperezgr/python-bigdata |
- We can convert it to a series thus: | weekly_rets_series = pd.Series(weekly_rets, index=weekly_prices.index[1:])
weekly_rets_series.head() | _____no_output_____ | CC-BY-4.0 | src/main/ipynb/pandas.ipynb | pperezgr/python-bigdata |
Plotting with the correct time axis Now when we plot the series we will obtain the correct time axis: | plt.plot(weekly_rets_series)
plt.title('GOOGL weekly log-returns'); plt.xlabel('t'); plt.ylabel('$r_t$')
plt.show() | _____no_output_____ | CC-BY-4.0 | src/main/ipynb/pandas.ipynb | pperezgr/python-bigdata |
Plotting a return histogram | weekly_rets_series.hist()
plt.show()
weekly_rets_series.describe() | _____no_output_____ | CC-BY-4.0 | src/main/ipynb/pandas.ipynb | pperezgr/python-bigdata |
Scraping Fantasy Football DataNeed to scrape the following data:- Weekly Player PPR Projections: ESPN, CBS, Fantasy Sharks, Scout Fantasy Sporsts, (and tried Fantasy Football Today but doesn't have defense projections currently, so exclude)- Previous Week Player Actual PPR Results- Weekly Fanduel Player Salary (can ma... | import pandas as pd
import numpy as np
import requests
# import json
# from bs4 import BeautifulSoup
import time
from selenium import webdriver
from selenium.webdriver.common.by import By
from selenium.webdriver.support.ui import WebDriverWait
from selenium.webdriver.support import expected_conditions as EC
# from se... | _____no_output_____ | MIT | data/Scraping Fantasy Football Data - FINAL.ipynb | zgscherrer/Project-Fantasy-Football |
Get Weekly Player Actual Fantasy PPR PointsGet from ESPN's Scoring Leaders tablehttp://games.espn.com/ffl/leaders?&scoringPeriodId=1&seasonId=2018&slotCategoryId=0&leagueID=0- scoringPeriodId = week of the season- seasonId = year- slotCategoryId = position, where 'QB':0, 'RB':2, 'WR':4, 'TE':6, 'K':17, 'D/ST':16- leag... | ##SCRAPE ESPN SCORING LEADERS TABLE FOR ACTUAL FANTASY PPR POINTS##
#input needs to be year as four digit number and week as number
#returns dataframe of scraped data
def scrape_actual_PPR_player_points_ESPN(week, year):
#instantiate the driver
driver = instantiate_selenium_driver()
#initialize dataf... | Pickle saved to: pickle_archive/Week1_Player_Actual_PPR_messy_scrape_2018-9-16-7-31.pkl
Pickle saved to: pickle_archive/Week1_Player_Actual_PPR_2018-9-16-7-31.pkl
(1007, 5)
| MIT | data/Scraping Fantasy Football Data - FINAL.ipynb | zgscherrer/Project-Fantasy-Football |
Get ESPN Player Fantasy Points Projections for Week Get from ESPN's Projections Tablehttp://games.espn.com/ffl/tools/projections?&scoringPeriodId=1&seasonId=2018&slotCategoryId=0&leagueID=0- scoringPeriodId = week of the season- seasonId = year- slotCategoryId = position, where 'QB':0, 'RB':2, 'WR':4, 'TE':6, 'K':17, ... | ##SCRAPE ESPN PROJECTIONS TABLE FOR PROJECTED FANTASY PPR POINTS##
#input needs to be year as four digit number and week as number
#returns dataframe of scraped data
def scrape_weekly_player_projections_ESPN(week, year):
#instantiate the driver on the ESPN projections page
driver = instantiate_selenium_driver... | Pickle saved to: pickle_archive/Week2_PPR_Projections_ESPN_messy_scrape_2018-9-16-7-35.pkl
Pickle saved to: pickle_archive/Week2_PPR_Projections_ESPN_2018-9-16-7-35.pkl
(1007, 5)
| MIT | data/Scraping Fantasy Football Data - FINAL.ipynb | zgscherrer/Project-Fantasy-Football |
Get CBS Player Fantasy Points Projections for Week Get from CBS's Projections Tablehttps://www.cbssports.com/fantasy/football/stats/sortable/points/QB/ppr/projections/2018/2?&print_rows=9999- QB is where position goes- 2018 is where season goes- 2 is where week goes- print_rows = 9999 gives all results in one table | ##SCRAPE CBS PROJECTIONS TABLE FOR PROJECTED FANTASY PPR POINTS##
#input needs to be year as four digit number and week as number
#returns dataframe of scraped data
def scrape_weekly_player_projections_CBS(week, year):
###GET PROJECTIONS FROM CBS###
#CBS has separate tables for each position, so need to cycle... | Pickle saved to: pickle_archive/Week2_PPR_Projections_CBS_messy_scrape_2018-9-16-7-35.pkl
Pickle saved to: pickle_archive/Week2_PPR_Projections_CBS_2018-9-16-7-35.pkl
(815, 5)
| MIT | data/Scraping Fantasy Football Data - FINAL.ipynb | zgscherrer/Project-Fantasy-Football |
Get Fantasy Sharks Player Points Projection for WeekThey have a json option that gets updated weekly (don't appear to store previous week projections). The json defaults to PPR (which is lucky for us) and has an all players option.https://www.fantasysharks.com/apps/Projections/WeeklyProjections.php?pos=ALL&format=jso... | ##SCRAPE FANTASY SHARKS PROJECTIONS TABLE FOR PROJECTED FANTASY PPR POINTS##
#input needs to be week as number (year isn't used, but keep same format as others)
#returns dataframe of scraped data
def scrape_weekly_player_projections_Sharks(week, year):
#fantasy sharks url - segment for 2018 week 1 starts at 628 an... | Pickle saved to: pickle_archive/Week2_PPR_Projections_Sharks_messy_scrape_2018-9-16-7-35.pkl
Pickle saved to: pickle_archive/Week2_PPR_Projections_Sharks_2018-9-16-7-35.pkl
(992, 5)
| MIT | data/Scraping Fantasy Football Data - FINAL.ipynb | zgscherrer/Project-Fantasy-Football |
Get Scout Fantasy Sports Player Fantasy Points Projections for Week Get from Scout Fantasy Sports Projections Tablehttps://fftoolbox.scoutfantasysports.com/football/rankings/?pos=rb&week=2&noppr=false- pos is position with options of 'QB','RB','WR','TE', 'K', 'DEF'- week is week of year- noppr is set to false when you... | ##SCRAPE Scout PROJECTIONS TABLE FOR PROJECTED FANTASY PPR POINTS##
#input needs to be year as four digit number and week as number
#returns dataframe of scraped data
def scrape_weekly_player_projections_SCOUT(week, year):
###GET PROJECTIONS FROM SCOUT###
#SCOUT has separate tables for each position, so need ... | C:\Users\micha\Anaconda3\envs\PythonData\lib\site-packages\urllib3\connectionpool.py:858: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings
InsecureRequestWarning)
C:\Users... | MIT | data/Scraping Fantasy Football Data - FINAL.ipynb | zgscherrer/Project-Fantasy-Football |
Get FanDuel Player Salaries for Week just import the Thurs-Mon game salaries (they differ for each game type, and note they don't include Kickers in the Thurs-Mon)Go to a FanDuel Thurs-Mon competition and Download a csv of players, which we then upload and format in python. | ###FORMAT/EXTRACT FANDUEL SALARY INFO###
def format_extract_FanDuel(df_fanduel_csv, week, year):
#rename columns
df_fanduel_csv.rename(columns={'Position':'POS',
'Nickname':'PLAYER',
'Team':'TEAM',
'Sala... | Pickle saved to: pickle_archive/Week2_Salary_FanDuel_2018-9-16-7-35.pkl
(669, 5)
| MIT | data/Scraping Fantasy Football Data - FINAL.ipynb | zgscherrer/Project-Fantasy-Football |
!!!FFtoday apparently doesn't do weekly projections for Defenses, so don't use it for now (can check back in future and see if updated)!!! Get FFtoday Player Fantasy Points Projections for Week Get from FFtoday's Projections Tablehttp://www.fftoday.com/rankings/playerwkproj.php?Season=2018&GameWeek=2&PosID=10&LeagueID... | # ##SCRAPE FFtoday PROJECTIONS TABLE FOR PROJECTED FANTASY PPR POINTS##
# #input needs to be year as four digit number and week as number
# #returns dataframe of scraped data
# def scrape_weekly_player_projections_FFtoday(week, year):
# #instantiate selenium driver
# driver = instantiate_selenium_driver()
... | _____no_output_____ | MIT | data/Scraping Fantasy Football Data - FINAL.ipynb | zgscherrer/Project-Fantasy-Football |
Initial Database Stuff | # actual_ppr_df = pd.read_pickle('pickle_archive/Week1_Player_Actual_PPR_2018-9-13-6-41.pkl')
# espn_final_df = pd.read_pickle('pickle_archive/Week1_PPR_Projections_ESPN_2018-9-13-6-46.pkl')
# cbs_final_df = pd.read_pickle('pickle_archive/Week1_PPR_Projections_CBS_2018-9-13-17-45.pkl')
# cbs_final_df.head()
# from sqla... | _____no_output_____ | MIT | data/Scraping Fantasy Football Data - FINAL.ipynb | zgscherrer/Project-Fantasy-Football |
Tutorial 13: Skyrmion in a disk> Interactive online tutorial:> [](https://mybinder.org/v2/gh/ubermag/oommfc/master?filepath=docs%2Fipynb%2Findex.ipynb) In this tutorial, we compute and relax a skyrmion in a interfacial-DMI material in a confined disk like geometry. | import discretisedfield as df
import micromagneticmodel as mm
import oommfc as oc | _____no_output_____ | BSD-3-Clause | docs/ipynb/13-tutorial-skyrmion.ipynb | ubermag/mumaxc |
We define mesh in cuboid through corner points `p1` and `p2`, and discretisation cell size `cell`. | region = df.Region(p1=(-50e-9, -50e-9, 0), p2=(50e-9, 50e-9, 10e-9))
mesh = df.Mesh(region=region, cell=(5e-9, 5e-9, 5e-9)) | _____no_output_____ | BSD-3-Clause | docs/ipynb/13-tutorial-skyrmion.ipynb | ubermag/mumaxc |
The mesh we defined is: | %matplotlib inline
mesh.k3d() | _____no_output_____ | BSD-3-Clause | docs/ipynb/13-tutorial-skyrmion.ipynb | ubermag/mumaxc |
Now, we can define the system object by first setting up the Hamiltonian: | system = mm.System(name="skyrmion")
system.energy = (
mm.Exchange(A=1.6e-11)
+ mm.DMI(D=4e-3, crystalclass="Cnv")
+ mm.UniaxialAnisotropy(K=0.51e6, u=(0, 0, 1))
+ mm.Demag()
+ mm.Zeeman(H=(0, 0, 2e5))
) | _____no_output_____ | BSD-3-Clause | docs/ipynb/13-tutorial-skyrmion.ipynb | ubermag/mumaxc |
Disk geometry is set up be defining the saturation magnetisation (norm of the magnetisation field). For that, we define a function: | Ms = 1.1e6
def Ms_fun(pos):
"""Function to set magnitude of magnetisation: zero outside cylindric shape,
Ms inside cylinder.
Cylinder radius is 50nm.
"""
x, y, z = pos
if (x**2 + y**2) ** 0.5 < 50e-9:
return Ms
else:
return 0 | _____no_output_____ | BSD-3-Clause | docs/ipynb/13-tutorial-skyrmion.ipynb | ubermag/mumaxc |
And the second function we need is the function to definr the initial magnetisation which is going to relax to skyrmion. | def m_init(pos):
"""Function to set initial magnetisation direction:
-z inside cylinder (r=10nm),
+z outside cylinder.
y-component to break symmetry.
"""
x, y, z = pos
if (x**2 + y**2) ** 0.5 < 10e-9:
return (0, 0, -1)
else:
return (0, 0, 1)
# create system with above ... | _____no_output_____ | BSD-3-Clause | docs/ipynb/13-tutorial-skyrmion.ipynb | ubermag/mumaxc |
The geometry is now: | system.m.norm.k3d_nonzero() | _____no_output_____ | BSD-3-Clause | docs/ipynb/13-tutorial-skyrmion.ipynb | ubermag/mumaxc |
and the initial magnetsation is: | system.m.plane("z").mpl() | _____no_output_____ | BSD-3-Clause | docs/ipynb/13-tutorial-skyrmion.ipynb | ubermag/mumaxc |
Finally we can minimise the energy and plot the magnetisation. | # minimize the energy
md = oc.MinDriver()
md.drive(system)
# Plot relaxed configuration: vectors in z-plane
system.m.plane("z").mpl()
# Plot z-component only:
system.m.z.plane("z").mpl()
# 3d-plot of z-component
system.m.z.k3d_voxels(filter_field=system.m.norm) | _____no_output_____ | BSD-3-Clause | docs/ipynb/13-tutorial-skyrmion.ipynb | ubermag/mumaxc |
Finally we can sample and plot the magnetisation along the line: | system.m.z.line(p1=(-49e-9, 0, 0), p2=(49e-9, 0, 0), n=20).mpl() | _____no_output_____ | BSD-3-Clause | docs/ipynb/13-tutorial-skyrmion.ipynb | ubermag/mumaxc |
**Author: Avani Gupta Roll: 2019121004** Excercise 2In Excercise 1, we computed the LDA for a multi-class problem, the IRIS dataset. In this excercise, we will now compare the LDA and PCA for the IRIS dataset.To revisit, the iris dataset contains measurements for 150 iris flowers from three different species.The three ... | from sklearn.datasets import make_classification
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns; sns.set();
import pandas as pd
from sklearn.model_selection import train_test_split
from numpy import pi | _____no_output_____ | MIT | HW9/2019121004_LDAExcercise2.ipynb | avani17101/SMAI-Assignments |
Importing the dataset | url = "https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data"
names = ['sepal-length', 'sepal-width', 'petal-length', 'petal-width', 'Class']
dataset = pd.read_csv(url, names=names)
dataset.tail() | _____no_output_____ | MIT | HW9/2019121004_LDAExcercise2.ipynb | avani17101/SMAI-Assignments |
Data preprocessingOnce dataset is loaded into a pandas data frame object, the first step is to divide dataset into features and corresponding labels and then divide the resultant dataset into training and test sets. The following code divides data into labels and feature set: | X = dataset.iloc[:, 0:4].values
y = dataset.iloc[:, 4].values | _____no_output_____ | MIT | HW9/2019121004_LDAExcercise2.ipynb | avani17101/SMAI-Assignments |
The above script assigns the first four columns of the dataset i.e. the feature set to X variable while the values in the fifth column (labels) are assigned to the y variable.The following code divides data into training and test sets: | from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0) | _____no_output_____ | MIT | HW9/2019121004_LDAExcercise2.ipynb | avani17101/SMAI-Assignments |
Feature ScalingWe will now perform feature scaling as part of data preprocessing too. For this task, we will be using scikit learn `StandardScalar`. | from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
| _____no_output_____ | MIT | HW9/2019121004_LDAExcercise2.ipynb | avani17101/SMAI-Assignments |
Write your code belowWrite your code to compute the PCA and LDA on the IRIS dataset below. | ### WRITE YOUR CODE HERE ####
from sklearn.preprocessing import LabelEncoder
enc = LabelEncoder()
label_encoder = enc.fit(y_train)
y_train = label_encoder.transform(y_train) + 1 #labels are done in alphabetical order
# 1: 'Iris-setosa', 2: 'Iris-versicolor', 3:'Iris-virginica'
label_encoder = enc.fit(y_test)
y_test = ... | Accuracy on train set 85.0
| MIT | HW9/2019121004_LDAExcercise2.ipynb | avani17101/SMAI-Assignments |
Exercise 11 - Recurrent Neural Networks========A recurrent neural network (RNN) is a class of neural network that excels when your data can be treated as a sequence - such as text, music, speech recognition, connected handwriting, or data over a time period. RNN's can analyse or predict a word based on the previous wor... | %%capture
# Run this!
from keras.models import load_model
from keras.models import Sequential
from keras.layers import Dense, Activation, LSTM
from keras.callbacks import LambdaCallback, ModelCheckpoint
import numpy as np
import random, sys, io, string | _____no_output_____ | MIT | 11. Recurrent Neural Networks - Python.ipynb | AnneliesseMorales/ms-learn-ml-crash-course-python |
Replace the `` with `The Time Machine` | ###
# REPLACE THE <addFileName> BELOW WITH The Time Machine
###
text = io.open('Data/<addFileName>.txt', encoding = 'UTF-8').read()
###
# Let's have a look at some of the text
print(text[0:198])
# This cuts out punctuation and make all the characters lower case
text = text.lower().translate(str.maketrans("", "", stri... | _____no_output_____ | MIT | 11. Recurrent Neural Networks - Python.ipynb | AnneliesseMorales/ms-learn-ml-crash-course-python |
Expected output: ```The Time Traveller (for so it will be convenient to speak of him) was expounding a recondite matter to us. His pale grey eyes shone and twinkled, and his usually pale face was flushed and animated.text length: 174201 charactersunique characters: 39```Step 2-----Next we'll divide the text into seque... | ###
# REPLACE <sequenceLength> WITH 40 AND <step> WITH 4
###
sequence_length = <sequenceLength>
step = <step>
###
sequences = []
target_chars = []
for i in range(0, len(text) - sequence_length, step):
sequences.append([text[i: i + sequence_length]])
target_chars.append(text[i + sequence_length])
print('number ... | _____no_output_____ | MIT | 11. Recurrent Neural Networks - Python.ipynb | AnneliesseMorales/ms-learn-ml-crash-course-python |
Expected output:`number of training sequences: 43541` Replace `` with `sequences` and run the code. | # One-hot vectorise
X = np.zeros((len(sequences), sequence_length, len(charset)), dtype=np.bool)
y = np.zeros((len(sequences), len(charset)), dtype=np.bool)
###
# REPLACE THE <addSequences> BELOW WITH sequences
###
for n, sequence in enumerate(<addSequences>):
###
for m, character in enumerate(list(sequence[0])):... | _____no_output_____ | MIT | 11. Recurrent Neural Networks - Python.ipynb | AnneliesseMorales/ms-learn-ml-crash-course-python |
Step 3------Let's build our model, using a single LSTM layer of 128 units. We'll keep the model simple for now, so that training does not take too long. In the cell below replace: 1. `` with `LSTM` 2. `` with `128` 3. `` with `'softmax` and then __run the code__. | model = Sequential()
###
# REPLACE THE <addLSTM> BELOW WITH LSTM (use uppercase) AND <addLayerSize> WITH 128
###
model.add(<addLSTM>(<addLayerSize>, input_shape = (X.shape[1], X.shape[2])))
###
###
# REPLACE THE <addSoftmaxFunction> with 'softmax' (INCLUDING THE QUOTES)
###
model.add(Dense(y.shape[1], activation = <a... | _____no_output_____ | MIT | 11. Recurrent Neural Networks - Python.ipynb | AnneliesseMorales/ms-learn-ml-crash-course-python |
The code below generates text at the end of an epoch (one training cycle). This allows us to see how the model is performing as it trains. If you're making a large neural network with a long training time it's useful to check in on the model as see if the text generating is legible as it trains, as overtraining may occ... | # Run this, but do not edit.
# It helps generate the text and save the model epochs.
# Generate new text
def on_epoch_end(epoch, _):
diversity = 0.5
print('\n### Generating text with diversity %0.2f' %(diversity))
start = random.randint(0, len(text) - sequence_length - 1)
seed = text[start: start + se... | _____no_output_____ | MIT | 11. Recurrent Neural Networks - Python.ipynb | AnneliesseMorales/ms-learn-ml-crash-course-python |
The code below will start the model to train. This may take a long time. Feel free to stop the training with the `square stop button` to the right of the `Run button` in the toolbar.Later in the exercise, we will load a pretrained model. In the cell below replace: 1. `` with `print_callback` 2. `` with `checkpoint` and... | ###
# REPLACE <addPrintCallback> WITH print_callback AND <addCheckpoint> WITH checkpoint
###
model.fit(X, y, batch_size = 128, epochs = 3, callbacks = [<addPrintCallback>, <addCheckpoint>])
### | _____no_output_____ | MIT | 11. Recurrent Neural Networks - Python.ipynb | AnneliesseMorales/ms-learn-ml-crash-course-python |
The output won't appear to be very good. But then, this dataset is small, and we have trained it only for a short time using a rather small RNN. How might it look if we upscaled things?Step 5------We could improve our model by:* Having a larger training set.* Increasing the number of LSTM units.* Training it for longer... | from keras.models import load_model
print("loading model... ", end = '')
###
# REPLACE <addLoadModel> BELOW WITH load_model
###
model = <addLoadModel>('Models/arthur-model-epoch-30.hdf5')
###
model.compile(loss = 'categorical_crossentropy', optimizer = 'Adam')
###
print("model loaded") | _____no_output_____ | MIT | 11. Recurrent Neural Networks - Python.ipynb | AnneliesseMorales/ms-learn-ml-crash-course-python |
Step 6-------Now let's use this model to generate some new text! Replace `` with `'Data/Arthur tales.txt'` | ###
# REPLACE <addFilePath> BELOW WITH 'Data/Arthur tales.txt' (INCLUDING THE QUOTATION MARKS)
###
text = io.open(<addFilePath>, encoding='UTF-8').read()
###
# Cut out punctuation and make lower case
text = text.lower().translate(str.maketrans("", "", string.punctuation))
# Character index dictionary
charset = sorted... | _____no_output_____ | MIT | 11. Recurrent Neural Networks - Python.ipynb | AnneliesseMorales/ms-learn-ml-crash-course-python |
In the cell below replace: 1. `` with `50` 2. `` with a sentence of your own, at least 50 characters long. 3. `` with the number of characters you want to generate (choose a large number, like 1500) and then __run the code__. | # Generate text
diversity = 0.5
print('\n### Generating text with diversity %0.2f' %(diversity))
###
# REPLACE <sequenceLength> BELOW WITH 50
###
sequence_length = <sequenceLength>
###
# Next we'll make a starting point for our text generator
###
# REPLACE <writeSentence> WITH A SENTENCE OF AT LEAST 50 CHARACTERS
#... | _____no_output_____ | MIT | 11. Recurrent Neural Networks - Python.ipynb | AnneliesseMorales/ms-learn-ml-crash-course-python |
Introduction to Python and Natural Language Technologies__Laboratory 10- NLP applications, Dependency parsing____April 22, 2021__During this laboratory you will have to implement various evaluation methods and use them to measure the performance of pretrained models. | import stanza
import spacy
from gensim.summarization import summarizer as gensim_summarizer
from transformers import pipeline
import nltk
import conllu
import os
import numpy as np
import requests
stanza.download('en')
stanza_nlp = stanza.Pipeline('en')
spacy_nlp = spacy.load("en_core_web_sm") | _____no_output_____ | MIT | assignments/10_NLP_applications_lab.ipynb | bmeaut/python_nlp_2021_spring |
Let's download the UD treebanks if you do not have them already. We are going to use them for evaluations. | url = "https://lindat.mff.cuni.cz/repository/xmlui/bitstream/handle/11234/1-3424/ud-treebanks-v2.7.tgz"
tgz = 'ud-treebanks-v2.7.tgz'
directory = 'ud_treebanks'
if not os.path.exists(directory):
import tarfile
response = requests.get(url, stream=True)
with open(tgz, 'wb') as ud:
ud.write(response.co... | _____no_output_____ | MIT | assignments/10_NLP_applications_lab.ipynb | bmeaut/python_nlp_2021_spring |
Evaluation Methods 1. F-scoreProbably the most relevant measure we can use when we are evaluating classifiers.Implement the function below. The function takes two iterables and returns a detailed dictionary that contains the True Positive, True Negative, False Positive, Precision, Recall, F-score values for each uniq... | f_dict = {
0: {'tp': 4, 'fp': 0, 'fn': 0, 'precision': 1.0, 'recall': 1.0, 'f': 1.0},
1: {'tp': 4, 'fp': 0, 'fn': 0, 'precision': 1.0, 'recall': 1.0, 'f': 1.0},
2: {'tp': 4, 'fp': 0, 'fn': 0, 'precision': 1.0, 'recall': 1.0, 'f': 1.0},
'MICRO AVG': {'precision': 1.0, 'recall': 1.0, 'f': 1.0},
'M... | _____no_output_____ | MIT | assignments/10_NLP_applications_lab.ipynb | bmeaut/python_nlp_2021_spring |
1.1 Evaluate a pretrained POS tagger using the exampleChoose an existing POS tagger (eg. stanza, spacy, nltk) and predict the POS tags of the sentence given below. Compare the results to the refference below using the f_score function above. Keep in mind, that there are different POS formats, and you should compare th... | sentence = trees[0].metadata["text"]
upos = [token['upos'] for token in trees[0]]
xpos = [token['xpos'] for token in trees[0]]
print(f'{sentence}\n{upos}\n{xpos}')
# Your solution here | _____no_output_____ | MIT | assignments/10_NLP_applications_lab.ipynb | bmeaut/python_nlp_2021_spring |
2. ROUGE-N scoreWe usually use the ROUGE score to evaluate summaries, comparing the reference summaries and the generated summaries. Write a function that gets a reference summary, a generated summary and a number N. The number represents the length of n-grams to compare. The function should return a dictionary contai... | n2 = {'precision': 0.75, 'recall': 0.6, 'f': 0.6666666666666665}
def get_ngram(text, n):
raise NotImplementedError()
def rouge_n(reference, generated, n):
raise NotImplementedError()
reference = 'this cat is absoultely adorable today'
generated = 'this cat is adorable today'
assert n2 == rouge_n(reference, ge... | _____no_output_____ | MIT | assignments/10_NLP_applications_lab.ipynb | bmeaut/python_nlp_2021_spring |
2.1 Evaluate a pretraied summarizer using the exampleChoose a summarizer (eg. gensim, huggingface) and summarize the following text (taken from the [CNN-Daily Mail dataset](https://cs.nyu.edu/~kcho/DMQA/)) and calculate the ROUGE-2 score of the summary. | article = """Manchester City starlet Devante Cole, son of Andy Cole, has joined Barnsley on loan until January.
City have also confirmed that £3m midfielder Bruno Zuculini has joined Valencia on loan for the rest of the season.
Meanwhile Juventus and Roma remain keen on signing Matija Nastasic.
On the move: Manchester... | _____no_output_____ | MIT | assignments/10_NLP_applications_lab.ipynb | bmeaut/python_nlp_2021_spring |
3. Dependency parse evaluationWe've discussed the two methods used to evaluate dependency parsers.Reminder: - Labeled attachment score (LAS): the percentage of words that are assigned both the correct syntactic head and the correct dependency label - Unlabeled attachment score (UAS): the percentage of words that are a... | def convert_conllu(tree):
return {token['id']: (token['head'], token['deprel']) for token in tree}
reference_graph = convert_conllu(trees[0])
reference_graph
pred = {1: (0, 'root'), 2: (1, 'punct'), 3: (1, 'flat'), 4: (1, 'punct'), 5: (6, 'amod'),
6: (7, 'obj'), 7: (1, 'parataxis'), 8: (7, 'obj'), 9: (8, 'f... | _____no_output_____ | MIT | assignments/10_NLP_applications_lab.ipynb | bmeaut/python_nlp_2021_spring |
3.2 LAS methodImplement the LAS method as well, similarly to the previous evaluation script. | def las(gold, predicted):
raise NotImplementedError()
assert 26/29 == uas(reference_graph, pred)
assert 24/29 == las(reference_graph, pred) | _____no_output_____ | MIT | assignments/10_NLP_applications_lab.ipynb | bmeaut/python_nlp_2021_spring |
================ PASSING LEVEL ==================== 3.3 Try out the evaluation methods with stanzaEvaluate the predictions of stanza on the given example! To do so, you will have to convert the output of stanza to be in the same format as the expected input of the uas and las methods. We recomend the stanza [document... | def stanza_converter(stanza_doc):
raise NotImplementedError()
# Your solution here | _____no_output_____ | MIT | assignments/10_NLP_applications_lab.ipynb | bmeaut/python_nlp_2021_spring |
3.4 Compare the accuracy of stanza and spacyRun the spacy dependency parser on the same input as before and evaluate the performace. To do so you will have to implement a function, that converts the output of spacy (see the [documentation](https://spacy.io/usage/linguistic-featuresdependency-parse)) to the appropriate... | def spacy_converter(spacy_doc):
raise NotImplementedError()
# Your solution here | _____no_output_____ | MIT | assignments/10_NLP_applications_lab.ipynb | bmeaut/python_nlp_2021_spring |
Construction | import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
def sign(x):
return (-1)**(x < 0)
def make_standard(X):
means = X.mean(0)
stds = X.std(0)
return (X - means)/stds
class RegularizedRegression:
def __init__(self, name = None):
self.name = name
def rec... | _____no_output_____ | MIT | content/c2/.ipynb_checkpoints/construction-checkpoint.ipynb | JeffFessler/mlbook |
PROBLEM 1 INTRODUCTION | #Say "Hello, World!" With Python
print("Hello, World!")
#Python If-Else
#!/bin/python3
import math
import os
import random
import re
import sys
if __name__ == '__main__':
n = int(input().strip())
if 1 <= n <= 100:
if n % 2 != 0 or (n % 2 == 0 and 6<=n<=20):
print("Weird")
elif n % 2 == 0 and (2<... | _____no_output_____ | MIT | scripts.ipynb | giuliacasale/homework1 |
BASIC DATA TYPES | # List Comprehension
if __name__ == '__main__':
x = int(input())
y = int(input())
z = int(input())
n = int(input())
lista = [[i,j,k] for i in range(0,x+1) for j in range(0,y+1) for k in range(0,z+1) if i+j+k != n]
print(lista)
#Find the runner up score!
if __name__ == '__main__':
n = int(input()... | _____no_output_____ | MIT | scripts.ipynb | giuliacasale/homework1 |
STRINGS | #sWAP cASE
def swap_case(s):
new = ''
for char in s:
if char.isupper():
new += char.lower()
elif char.islower():
new += char.upper()
else:
new += char
return new
if __name__ == '__main__':
s = input()
result = swap_case(s)
print(resul... | _____no_output_____ | MIT | scripts.ipynb | giuliacasale/homework1 |
SETS | #Introduction to sets
def average(array):
# your code goes here
if 1<=len(array)<=100:
somma = 0
array1 = []
for elem in array:
if elem not in array1:
array1.append(elem)
somma += elem
average = somma/len(array1)
return average... | _____no_output_____ | MIT | scripts.ipynb | giuliacasale/homework1 |
COLLECTIONS | #collections.Counter()
# Enter your code here. Read input from STDIN. Print output to STDOUT
from collections import Counter
X = int(input()) #number of shoes
shoe_sizes = list(map(int,input().split()))
N = int(input()) #number of customers
shoe_sizes = Counter(shoe_sizes)
total = 0
if 0<X<10**3 and 0<N<=10**3... | _____no_output_____ | MIT | scripts.ipynb | giuliacasale/homework1 |
DATE AND TIME | #Calendar Module
# Enter your code here. Read input from STDIN. Print output to STDOUT
import calendar
month,day,year = map(int,input().split())
if 2000<year<3000:
weekdays = ['MONDAY','TUESDAY','WEDNESDAY','THURSDAY','FRIDAY','SATURDAY','SUNDAY']
weekday = calendar.weekday(year,month,day)
print(weekdays[... | _____no_output_____ | MIT | scripts.ipynb | giuliacasale/homework1 |
EXCEPTIONS | #Exceptions
# Enter your code here. Read input from STDIN. Print output to STDOUT
t = int(input())
if 0<t<10:
for i in range(t):
values = list(input().split())
try:
division = int(values[0])//int(values[1])
print(division)
except ZeroDivisionError as e:
... | _____no_output_____ | MIT | scripts.ipynb | giuliacasale/homework1 |
BUILT-INS | #Zipped!
# Enter your code here. Read input from STDIN. Print output to STDOUT
num_students, num_subjects = map(int,input().split())
mark_sheet = []
for i in range(num_subjects):
mark_sheet.append(map(float, input().split(' ')))
for grades in zip(*mark_sheet):
somma = sum(grades)
print(somma/num_subjects... | _____no_output_____ | MIT | scripts.ipynb | giuliacasale/homework1 |
PYTHON FUNCTIONALS | #Map and Lambda Functions
cube = lambda x: x**3 # complete the lambda function
def fibonacci(n):
# return a list of fibonacci numbers
serie = []
if 0<=n<=15:
if n == 1:
serie = [0]
if n > 1:
serie = [0, 1]
for i in range(1,n-1):
serie.ap... | _____no_output_____ | MIT | scripts.ipynb | giuliacasale/homework1 |
REGEX AND PARSING | #Detect Floating Point Number
# Enter your code here. Read input from STDIN. Print output to STDOUT
import re
t = int(input())
if 0<t<10:
for i in range(t):
test_case = input()
print(bool(re.search(r"^[+-/.]?[0-9]*\.[0-9]+$",test_case)))
#Re.split()
regex_pattern = r"[,.]" # Do not delete 'r'.
... | _____no_output_____ | MIT | scripts.ipynb | giuliacasale/homework1 |
XML | #XML 1 - Find the score
import sys
import xml.etree.ElementTree as etree
def get_attr_number(node):
# your code goes here
somma = 0
for elem in node.iter():
diz = elem.attrib
somma += len(diz)
return somma
if __name__ == '__main__':
sys.stdin.readline()
xml = sys.stdin.read()
... | _____no_output_____ | MIT | scripts.ipynb | giuliacasale/homework1 |
CLOSURES AND DECORATIONS | #Standardize Mobile Number Using Decorators
import re
def wrapper(f):
def fun(l):
# complete the function
lista = []
for elem in l:
if len(elem) == 10:
lista.append('+91'+' '+str(elem[0:5]+ ' '+str(elem[5:])))
elif len(elem) == 11:
li... | _____no_output_____ | MIT | scripts.ipynb | giuliacasale/homework1 |
NUMPY | #Arrays
import numpy
def arrays(arr):
# complete this function
# use numpy.array
arr.reverse()
return numpy.array(arr, float)
arr = input().strip().split(' ')
result = arrays(arr)
print(result)
#Shape and Reshape
import numpy
x = list(map(int,input().split()))
my_array = numpy.array(x)
print(nump... | _____no_output_____ | MIT | scripts.ipynb | giuliacasale/homework1 |
PROBLEM 2 | #Birthday Cake Candles
#!/bin/python3
import math
import os
import random
import re
import sys
#
# Complete the 'birthdayCakeCandles' function below.
#
# The function is expected to return an INTEGER.
# The function accepts INTEGER_ARRAY candles as parameter.
#
def birthdayCakeCandles(candles):
# Write your cod... | _____no_output_____ | MIT | scripts.ipynb | giuliacasale/homework1 |
PRACTICA DE PREDICCION DE SERIES DE TIEMPO CON TENSORFLOW 1. Cargue Librerias y Data Set | # Cargamos las librerias necesarias para el analisis
import os
import datetime
import IPython
import IPython.display
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import tensorflow as tf
mpl.rcParams['figure.figsize'] = (8, 6)
mpl.rcParams['axes... | Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/jena_climate_2009_2016.csv.zip
13574144/13568290 [==============================] - 0s 0us/step
| MIT | Aprendizaje_Time_Series_con_Deep_Learning.ipynb | diegojeda/AdvancedMethodsDataAnalysisClass |
2. Limpieza y Preparacion De Datos | df = pd.read_csv(csv_path)
df.head()
# Ya que el registro esta cada 10 minutos, tomaremos solo el valor correspondiente al valor final de la hora, para tener solo un valor por hora
df = pd.read_csv(csv_path)
# slice [start:stop:step], starting from index 5 take every 6th record.
df = df[5::6]
# Convertimos la colum... | _____no_output_____ | MIT | Aprendizaje_Time_Series_con_Deep_Learning.ipynb | diegojeda/AdvancedMethodsDataAnalysisClass |
Podemos observar que las variables de "wv (m/s)" y "max. wv (m/s)" tienen valors minimos anomalos, estos deben ser erroneos, procederemos a imputarlos con cero. | wv = df['wv (m/s)']
bad_wv = wv == -9999.0
wv[bad_wv] = 0.0
max_wv = df['max. wv (m/s)']
bad_max_wv = max_wv == -9999.0
max_wv[bad_max_wv] = 0.0
df['wv (m/s)'].min() | _____no_output_____ | MIT | Aprendizaje_Time_Series_con_Deep_Learning.ipynb | diegojeda/AdvancedMethodsDataAnalysisClass |
La ultima variable "wd (deg)" nos indica la direccion del viento en grados. Sin embargo, los grados no son un buen input para el modelo. En este caso las limitaciones son las siguientes:- 0° y 360° deberian estar cerca y cerrarse, eso no se puede apreciar.- Si no hay velocidad del viento, la direccion no debería import... | plt.hist2d(df['wd (deg)'], df['wv (m/s)'], bins=(50, 50), vmax=400)
plt.colorbar()
plt.xlabel('Wind Direction [deg]')
plt.ylabel('Wind Velocity [m/s]'); | _____no_output_____ | MIT | Aprendizaje_Time_Series_con_Deep_Learning.ipynb | diegojeda/AdvancedMethodsDataAnalysisClass |
Para solventar estas dificultadades, convertiremos la direccion y la magnitud de la velocidad del viento en vectores, una medida mas representativa | wv = df.pop('wv (m/s)')
max_wv = df.pop('max. wv (m/s)')
# Convert to radians.
wd_rad = df.pop('wd (deg)')*np.pi / 180
# Calculate the wind x and y components.
df['Wx'] = wv*np.cos(wd_rad)
df['Wy'] = wv*np.sin(wd_rad)
# Calculate the max wind x and y components.
df['max Wx'] = max_wv*np.cos(wd_rad)
df['max Wy'] = ma... | _____no_output_____ | MIT | Aprendizaje_Time_Series_con_Deep_Learning.ipynb | diegojeda/AdvancedMethodsDataAnalysisClass |
Revisaremos la distribucion de los componentes de cada vector | plt.hist2d(df['Wx'], df['Wy'], bins=(50, 50), vmax=400)
plt.colorbar()
plt.xlabel('Wind X [m/s]')
plt.ylabel('Wind Y [m/s]')
ax = plt.gca()
ax.axis('tight')
# Transformaremos la fecha a segundos para revisar periodicidiad
timestamp_s = date_time.map(datetime.datetime.timestamp)
# Definimos los segundos que tiene un di... | _____no_output_____ | MIT | Aprendizaje_Time_Series_con_Deep_Learning.ipynb | diegojeda/AdvancedMethodsDataAnalysisClass |
Podemos evidenciar que los dos picos se presentan en 1/año y 1/dia, lo que corrobora nuestras suposiciones. | df.head() | _____no_output_____ | MIT | Aprendizaje_Time_Series_con_Deep_Learning.ipynb | diegojeda/AdvancedMethodsDataAnalysisClass |
3. Split De Los DatosDividiremos los datos de la siguiente manera:- Entrenamiento: 70%- Validacion: 20%- Prueba: 10% | column_indices = {name: i for i, name in enumerate(df.columns)}
n = len(df)
train_df = df[0:int(n*0.7)]
val_df = df[int(n*0.7):int(n*0.9)]
test_df = df[int(n*0.9):]
num_features = df.shape[1] | _____no_output_____ | MIT | Aprendizaje_Time_Series_con_Deep_Learning.ipynb | diegojeda/AdvancedMethodsDataAnalysisClass |
4. Normalizacion De Los Datos | # Normalizaremos los conjuntos de entrenamiento, validacion y prueba
train_mean = train_df.mean()
train_std = train_df.std()
train_df = (train_df - train_mean) / train_std
val_df = (val_df - train_mean) / train_std
test_df = (test_df - train_mean) / train_std
df_std = (df - train_mean) / train_std
df_std = df_std.melt... | _____no_output_____ | MIT | Aprendizaje_Time_Series_con_Deep_Learning.ipynb | diegojeda/AdvancedMethodsDataAnalysisClass |
Classification with Python In this notebook we try to practice all the classification algorithms that we learned in this course.We load a dataset using Pandas library, and apply the following algorithms, and find the best one for this specific dataset by accuracy evaluation methods.Lets first load required libraries: | import itertools
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.ticker import NullFormatter
import pandas as pd
import numpy as np
import matplotlib.ticker as ticker
from sklearn import preprocessing
%matplotlib inline | _____no_output_____ | MIT | Coursera/IBM Python 01/Course02/ML Python Sharing.ipynb | brianshen1990/KeepLearning |
About dataset This dataset is about past loans. The __Loan_train.csv__ data set includes details of 346 customers whose loan are already paid off or defaulted. It includes following fields:| Field | Description ||----------------|------... | !wget -O loan_train.csv https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/loan_train.csv | --2020-05-22 14:48:38-- https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/loan_train.csv
Resolving s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)... 67.228.254.196
Connecting to s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-ge... | MIT | Coursera/IBM Python 01/Course02/ML Python Sharing.ipynb | brianshen1990/KeepLearning |
Load Data From CSV File | df = pd.read_csv('loan_train.csv')
df.head()
df.shape | _____no_output_____ | MIT | Coursera/IBM Python 01/Course02/ML Python Sharing.ipynb | brianshen1990/KeepLearning |
Convert to date time object | df['due_date'] = pd.to_datetime(df['due_date'])
df['effective_date'] = pd.to_datetime(df['effective_date'])
df.head() | _____no_output_____ | MIT | Coursera/IBM Python 01/Course02/ML Python Sharing.ipynb | brianshen1990/KeepLearning |
Data visualization and pre-processing Let’s see how many of each class is in our data set | df['loan_status'].value_counts() | _____no_output_____ | MIT | Coursera/IBM Python 01/Course02/ML Python Sharing.ipynb | brianshen1990/KeepLearning |
260 people have paid off the loan on time while 86 have gone into collection Lets plot some columns to underestand data better: | # notice: installing seaborn might takes a few minutes
!conda install -c anaconda seaborn -y
import seaborn as sns
bins = np.linspace(df.Principal.min(), df.Principal.max(), 10)
g = sns.FacetGrid(df, col="Gender", hue="loan_status", palette="Set1", col_wrap=2)
g.map(plt.hist, 'Principal', bins=bins, ec="k")
g.axes[-1... | _____no_output_____ | MIT | Coursera/IBM Python 01/Course02/ML Python Sharing.ipynb | brianshen1990/KeepLearning |
Pre-processing: Feature selection/extraction Lets look at the day of the week people get the loan | df['dayofweek'] = df['effective_date'].dt.dayofweek
bins = np.linspace(df.dayofweek.min(), df.dayofweek.max(), 10)
g = sns.FacetGrid(df, col="Gender", hue="loan_status", palette="Set1", col_wrap=2)
g.map(plt.hist, 'dayofweek', bins=bins, ec="k")
g.axes[-1].legend()
plt.show()
| _____no_output_____ | MIT | Coursera/IBM Python 01/Course02/ML Python Sharing.ipynb | brianshen1990/KeepLearning |
We see that people who get the loan at the end of the week dont pay it off, so lets use Feature binarization to set a threshold values less then day 4 | df['weekend'] = df['dayofweek'].apply(lambda x: 1 if (x>3) else 0)
df.head() | _____no_output_____ | MIT | Coursera/IBM Python 01/Course02/ML Python Sharing.ipynb | brianshen1990/KeepLearning |
Convert Categorical features to numerical values Lets look at gender: | df.groupby(['Gender'])['loan_status'].value_counts(normalize=True) | _____no_output_____ | MIT | Coursera/IBM Python 01/Course02/ML Python Sharing.ipynb | brianshen1990/KeepLearning |
86 % of female pay there loans while only 73 % of males pay there loan Lets convert male to 0 and female to 1: | df['Gender'].replace(to_replace=['male','female'], value=[0,1],inplace=True)
df.head() | _____no_output_____ | MIT | Coursera/IBM Python 01/Course02/ML Python Sharing.ipynb | brianshen1990/KeepLearning |
One Hot Encoding How about education? | df.groupby(['education'])['loan_status'].value_counts(normalize=True) | _____no_output_____ | MIT | Coursera/IBM Python 01/Course02/ML Python Sharing.ipynb | brianshen1990/KeepLearning |
Feature befor One Hot Encoding | df[['Principal','terms','age','Gender','education']].head() | _____no_output_____ | MIT | Coursera/IBM Python 01/Course02/ML Python Sharing.ipynb | brianshen1990/KeepLearning |
Use one hot encoding technique to conver categorical varables to binary variables and append them to the feature Data Frame | Feature = df[['Principal','terms','age','Gender','weekend']]
Feature = pd.concat([Feature,pd.get_dummies(df['education'])], axis=1)
Feature.drop(['Master or Above'], axis = 1,inplace=True)
Feature.head()
| _____no_output_____ | MIT | Coursera/IBM Python 01/Course02/ML Python Sharing.ipynb | brianshen1990/KeepLearning |
Feature selection Lets defind feature sets, X: | X = Feature
X[0:5] | _____no_output_____ | MIT | Coursera/IBM Python 01/Course02/ML Python Sharing.ipynb | brianshen1990/KeepLearning |
What are our lables? | y = df['loan_status'].values
y[0:5] | _____no_output_____ | MIT | Coursera/IBM Python 01/Course02/ML Python Sharing.ipynb | brianshen1990/KeepLearning |
Normalize Data Data Standardization give data zero mean and unit variance (technically should be done after train test split ) | X= preprocessing.StandardScaler().fit(X).transform(X)
X[0:5] | /opt/conda/envs/Python36/lib/python3.6/site-packages/sklearn/preprocessing/data.py:645: DataConversionWarning: Data with input dtype uint8, int64 were all converted to float64 by StandardScaler.
return self.partial_fit(X, y)
/opt/conda/envs/Python36/lib/python3.6/site-packages/ipykernel/__main__.py:1: DataConversionW... | MIT | Coursera/IBM Python 01/Course02/ML Python Sharing.ipynb | brianshen1990/KeepLearning |
Classification Now, it is your turn, use the training set to build an accurate model. Then use the test set to report the accuracy of the modelYou should use the following algorithm:- K Nearest Neighbor(KNN)- Decision Tree- Support Vector Machine- Logistic Regression__ Notice:__ - You can go above and change the pre-... | from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.2, random_state=4)
print ('Train set:', X_train.shape, y_train.shape)
print ('Test set:', X_test.shape, y_test.shape) | Train set: (276, 8) (276,)
Test set: (70, 8) (70,)
| MIT | Coursera/IBM Python 01/Course02/ML Python Sharing.ipynb | brianshen1990/KeepLearning |
Calculate the best K among 1 to 15and plot the result to select | from sklearn.neighbors import KNeighborsClassifier
from sklearn import metrics
Ks = 15
mean_acc = np.zeros((Ks-1))
std_acc = np.zeros((Ks-1))
ConfustionMx = [];
for n in range(1,Ks):
#Train Model and Predict
neigh = KNeighborsClassifier(n_neighbors = n).fit(X_train,y_train)
yhat=neigh.predict(X_test)... | _____no_output_____ | MIT | Coursera/IBM Python 01/Course02/ML Python Sharing.ipynb | brianshen1990/KeepLearning |
The answerIt seems k = 7 gives the best accuracy Decision Tree Train set adn Test SetJust use the previous one~ And use Decision Tree to build the model (max_depeth from1 - 10 , why？ There are only less than 10 kinds of attributes ) | from sklearn.tree import DecisionTreeClassifier
from sklearn import metrics
import matplotlib.pyplot as plt
Ks = 10
acc = np.zeros((Ks-1))
for n in range(1,Ks):
drugTree = DecisionTreeClassifier(criterion="entropy", max_depth = n)
drugTree # it shows the default parameters
drugTree.fit(X_train,y_train)
... | _____no_output_____ | MIT | Coursera/IBM Python 01/Course02/ML Python Sharing.ipynb | brianshen1990/KeepLearning |
And we will use depth 6 with decision tree.??? Why 1 to 2 gives this results? Support Vector Machine Data pre-processing and selectionFor SVM, treat as numbers | Feature.dtypes
feature_df = Feature[['Principal', 'terms', 'age', 'Gender', 'weekend', 'Bechalor', 'High School or Below', 'college']]
X_SVM = np.asarray(feature_df)
X_SVM[0:5]
Y_Feature = [ 1 if i == "PAIDOFF" else 0 for i in df['loan_status'].values]
y_SVM = np.asarray(Y_Feature)
y_SVM [0:5] | _____no_output_____ | MIT | Coursera/IBM Python 01/Course02/ML Python Sharing.ipynb | brianshen1990/KeepLearning |
Train and Test dataSplit | X_train_SVM, X_test_SVM, y_train_SVM, y_test_SVM = train_test_split( X_SVM, y_SVM, test_size=0.2, random_state=4)
print ('Train set:', X_train_SVM.shape, y_train_SVM.shape)
print ('Test set:', X_test_SVM.shape, y_test_SVM.shape) | Train set: (276, 8) (276,)
Test set: (70, 8) (70,)
| MIT | Coursera/IBM Python 01/Course02/ML Python Sharing.ipynb | brianshen1990/KeepLearning |
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