As an amateur I have been working on a little coding project for fun. My goal is to extract some data from multiple URL's. I got to the point where I get the data I need from 1 URL (albeit pretty messy), but now I want to adjust my script so that I get data from URL's I want.
This is what my humble script currently looks like:
from bs4 import BeautifulSoup
import requests
url = "https://ktarena.com/fr/207-dofus-world-cup/match/46271/1"
page = requests.get(url)
soup = BeautifulSoup(page.content, 'html.parser')
for KTA in soup('img'):
KTAclass = KTA.get('title')
print(KTAclass)
for KTA in soup:
KTApoints = soup.findAll('div', class_="points")
print(KTApoints)
So, I would need to find a way to run this script on multiple URL's and collect all that data. As you can see the URL ends with 'match/46271/1'. I need to find a way to change the number in between the dashes; the first match I would want to analyze is 46271, the last would be 46394 (so around 120 URL's to analyze).
If anyone could help me in the right direction that would be greatly appreciated!
Try:
import requests
from bs4 import BeautifulSoup
def analyze(i):
url = f"https://ktarena.com/fr/207-dofus-world-cup/match/{i}/1"
page = requests.get(url)
soup = BeautifulSoup(page.content, "html.parser")
names = [a.text for a in soup.select(".name a")]
points = [p.text for p in soup.select(".result .points")]
print(url, *zip(names, points))
for i in range(46271, 46274): # <-- increase number here
analyze(i)
Prints:
https://ktarena.com/fr/207-dofus-world-cup/match/46271/1 ('Shadow Zoo', '0 pts') ('UndisClosed', '60 pts')
https://ktarena.com/fr/207-dofus-world-cup/match/46272/1 ('Laugh Tale', '0 pts') ('FromTheAbyss', '60 pts')
https://ktarena.com/fr/207-dofus-world-cup/match/46273/1 ('Motamawa', '0 pts') ('Espoo', '60 pts')
Related
I'm new. I'm trying to fine-tuned a BERT MLM (bert-base-uncased) on a target domain. Unfortunately, results are not good.
Before fine-tuning, the pre-trained model fills the mask of a sentence with words in line of human expectations.
E.g. Wikipedia is a free online [MASK], created and edited by volunteers around the world.
The most probable prediction are encyclopedia (score: 0.650) and resource (score:0.087).
After fine-tuning, the prediction are completely wrong. Often stopwords are predicted as result.
E.g. Wikipedia is a free online [MASK], created and edited by volunteers around the world.
The most probable prediction are the (score: 0.052) and be (score:0.033).
I experimented with different epochs (from 1 to 10) and different datasets (from a few MB to a few GB) but I got the same issue. What am I doing wrong? I'm using the following code, I hope you can help me.
from transformers import AutoConfig, AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')
config = AutoConfig.from_pretrained('bert-base-uncased', output_hidden_states=True)
model = AutoModelForMaskedLM.from_config(config) # BertForMaskedLM.from_pretrained(path)
from transformers import LineByLineTextDataset
dataset = LineByLineTextDataset(tokenizer=tokenizer,
file_path="data/english/corpora.txt",
block_size = 512)
from transformers import DataCollatorForLanguageModeling
data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=True, mlm_probability=0.15)
from transformers import Trainer, TrainingArguments
training_args = TrainingArguments(output_dir="output/models/english",
overwrite_output_dir=True,
num_train_epochs=5,
per_gpu_train_batch_size=8,
save_steps = 22222222,
save_total_limit=2)
trainer = Trainer(model=model, args=training_args, data_collator=data_collator, train_dataset=dataset)
trainer.train()
trainer.save_model("output/models/english")
from transformers import pipeline
# Initialize MLM pipeline
mlm = pipeline('fill-mask', model="output/models/english", tokenizer="output/models/english")
# Get mask token
mask = mlm.tokenizer.mask_token
# Get result for particular masked phrase
phrase = f'Wikipedia is a free online {mask}, created and edited by volunteers around the world'
result = mlm(phrase)
# Print result
print(result)
I'm running StatsModels to estimate parameters of a multiple regression model, using county-level data for 3085 counties. When I use statsmodels.formula.api, and drop a few rows from the data, I get desired results. All seems well enough.
import pandas as pd
import numpy as np
import statsmodels.formula.api as sm
%matplotlib inline
from statsmodels.compat import lzip
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(style="whitegrid")
eg=pd.read_csv(r'C:/Users/user/anaconda3/une_edu_pipc_06.csv')
pd.options.display.precision = 3
plt.rc("figure", figsize=(16,8))
plt.rc("font", size=14)
sm_col = eg["lt_hsd_17"] + eg["hsd_17"]
eg["ut_hsd_17"] = sm_col
sm_col2 = eg["sm_col_17"] + eg["col_17"]
eg["bnd_hsd_17"] = sm_col2
eg["d_09"]= eg["Rate_09"]-eg["Rate_06"]
eg["d_10"]= eg["Rate_10"]-eg["Rate_06"]
inc_2=eg["p_c_inc_18"]*eg["p_c_inc_18"]
res = sm.ols(formula = "Rate_18 ~ p_c_inc_18 + ut_hsd_17 + d_10 + inc_2",
data=eg, missing='drop').fit()
print(res.summary()).
(BTW, eg["p_c_inc_18"]is per-capita income, and inc_2 is p_c_inc_18 squarred).
But when I wish to use import statsmodels.api as smas the module, everything else staying pretty much the same, and run the following code after all appropriate preliminaries,
inc_2=eg["p_c_inc_18"]*eg["p_c_inc_18"]
X = eg[["p_c_inc_18","ut_hsd_17","d_10","inc_2"]]
y = eg["Rate_18"]
X = sm.add_constant(X)
mod = sm.OLS(y, X)
res = mod.fit()
print(res.summary())
then things fall apart, and the Python interpreter throws an error, as follows:
[......]
KeyError: "['inc_2'] not in index"
BTW, the only difference between the two 'runs' is that 15 rows are dropped during the first, successful, model run, while I don't as yet know how to drop missing rows from the second model formulation. Could that difference be responsible for why the second run fails? (I chose to omit large parts of the error message, to reduce clutter.)
You need to assign inc_2 in your DataFrame.
inc_2=eg["p_c_inc_18"]*eg["p_c_inc_18"]
should be
eg["inc_2"] = eg["p_c_inc_18"]*eg["p_c_inc_18"]
I am running 3 different model (Random forest, Gradient Boosting, Ada Boost) and a model ensemble based on these 3 models.
I managed to use SHAP for GB and RF but not for ADA with the following error:
Exception Traceback (most recent call last)
in engine
----> 1 explainer = shap.TreeExplainer(model,data = explain_data.head(1000), model_output= 'probability')
/home/cdsw/.local/lib/python3.6/site-packages/shap/explainers/tree.py in __init__(self, model, data, model_output, feature_perturbation, **deprecated_options)
110 self.feature_perturbation = feature_perturbation
111 self.expected_value = None
--> 112 self.model = TreeEnsemble(model, self.data, self.data_missing)
113
114 if feature_perturbation not in feature_perturbation_codes:
/home/cdsw/.local/lib/python3.6/site-packages/shap/explainers/tree.py in __init__(self, model, data, data_missing)
752 self.tree_output = "probability"
753 else:
--> 754 raise Exception("Model type not yet supported by TreeExplainer: " + str(type(model)))
755
756 # build a dense numpy version of all the tree objects
Exception: Model type not yet supported by TreeExplainer: <class 'sklearn.ensemble._weight_boosting.AdaBoostClassifier'>
I found this link on Git that state
TreeExplainer creates a TreeEnsemble object from whatever model type we are trying to explain, and then works with that downstream. So all you would need to do is and add another if statement in the
TreeEnsemble constructor similar to the one for gradient boosting
But I really don't know how to implement it since I quite new to this.
I had the same problem and what I did, was to modify the file in the git you are commenting.
In my case I use windows so the file is in C:\Users\my_user\AppData\Local\Continuum\anaconda3\Lib\site-packages\shap\explainers but you can do double click over the error message and the file will be opened.
The next step is to add another elif as the answer of the git help says. In my case I did it from the line 404 as following:
1) Modify the source code.
...
self.objective = objective_name_map.get(model.criterion, None)
self.tree_output = "probability"
elif str(type(model)).endswith("sklearn.ensemble.weight_boosting.AdaBoostClassifier'>"): #From this line I have modified the code
scaling = 1.0 / len(model.estimators_) # output is average of trees
self.trees = [Tree(e.tree_, normalize=True, scaling=scaling) for e in model.estimators_]
self.objective = objective_name_map.get(model.base_estimator_.criterion, None) #This line is done to get the decision criteria, for example gini.
self.tree_output = "probability" #This is the last line I added
elif str(type(model)).endswith("sklearn.ensemble.forest.ExtraTreesClassifier'>"): # TODO: add unit test for this case
scaling = 1.0 / len(model.estimators_) # output is average of trees
self.trees = [Tree(e.tree_, normalize=True, scaling=scaling) for e in model.estimators_]
...
Note in the other models, the code of shap needs the attribute 'criterion' that the AdaBoost classifier doesn't have in a direct way. So in this case this attribute is obtained from the "weak" classifiers with the AdaBoost has been trained, that's why I add model.base_estimator_.criterion .
Finally you have to import the library again, train your model and get the shap values. I leave an example:
2) Import again the library and try:
from sklearn import datasets
from sklearn.ensemble import AdaBoostClassifier
import shap
# import some data to play with
iris = datasets.load_iris()
X = iris.data
y = iris.target
ADABoost_model = AdaBoostClassifier()
ADABoost_model.fit(X, y)
shap_values = shap.TreeExplainer(ADABoost_model).shap_values(X)
shap.summary_plot(shap_values, X, plot_type="bar")
Which generates the following:
3) Get your new results:
It seems that the shap package has been updated and still does not contain the AdaBoostClassifier. Based on the previous answer, I've modified the previous answer to work with the shap/explainers/tree.py file in lines 598-610
### Added AdaBoostClassifier based on the outdated StackOverflow response and Github issue here
### https://stackoverflow.com/questions/60433389/how-to-calculate-shap-values-for-adaboost-model/61108156#61108156
### https://github.com/slundberg/shap/issues/335
elif safe_isinstance(model, ["sklearn.ensemble.AdaBoostClassifier", "sklearn.ensemble._weighted_boosting.AdaBoostClassifier"]):
assert hasattr(model, "estimators_"), "Model has no `estimators_`! Have you called `model.fit`?"
self.internal_dtype = model.estimators_[0].tree_.value.dtype.type
self.input_dtype = np.float32
scaling = 1.0 / len(model.estimators_) # output is average of trees
self.trees = [Tree(e.tree_, normalize=True, scaling=scaling) for e in model.estimators_]
self.objective = objective_name_map.get(model.base_estimator_.criterion, None) #This line is done to get the decision criteria, for example gini.
self.tree_output = "probability" #This is the last line added
Also working on testing to add this to the package :)
I am trying to create a bar graph in pygal that uses the api for github and charts the most popular projects based on stars. I posted my code below, but I cannot figure out why my graph keep saying "No Data"??? Any suggestions? Thanks!
import requests
import pygal
from pygal.style import LightColorizedStyle as LCS, LightenStyle as LS
url = 'https://api.github.com/search/repositories?q=language:python&sort=stars'
r = requests.get(url)
print("Status code:", r.status_code)
response_dict = r.json()
print('Total repositories:', response_dict['total_count'])
repo_dicts = response_dict['items']
names,stars = [],[]
for repo_dict in repo_dicts:
names.append(repo_dict['name'])
stars.append(repo_dict['stargazers_count'])
my_style = LS('#333366',base_style=LCS)
chart = pygal.Bar(style=my_style,x_label_rotation=45,show_legend=False)
chart.title = 'Most Starred Python Projects on GitHub'
chart.x_labels = names
chart.add = ('',stars)
chart.render_to_file('python_repos.svg')
on the second last line of your code, chart.add=('',stars) there should not be a '=' equal sign there , it should be chart.add('',stars) then the code should work! :)
I am trying to add more than 70000 new features to a genbank file using biopython.
I have this code:
from Bio import SeqIO
from Bio.SeqFeature import SeqFeature, FeatureLocation
fi = "myoriginal.gbk"
fo = "mynewfile.gbk"
for result in results:
start = 0
end = 0
result = result.split("\t")
start = int(result[0])
end = int(result[1])
for record in SeqIO.parse(original, "gb"):
record.features.append(SeqFeature(FeatureLocation(start, end), type = "misc_feat"))
SeqIO.write(record, fo, "gb")
Results is just a list of lists containing the start and end of each one of the features I need to add to the original gbk file.
This solution is extremely costly for my computer and I do not know how to improve the performance. Any good idea?
You should parse the genbank file just once. Omitting what results contains (I do not know exactly, because there are some missing pieces of code in your example), I would guess something like this would improve performance, modifying your code:
fi = "myoriginal.gbk"
fo = "mynewfile.gbk"
original_records = list(SeqIO.parse(fi, "gb"))
for result in results:
result = result.split("\t")
start = int(result[0])
end = int(result[1])
for record in original_records:
record.features.append(SeqFeature(FeatureLocation(start, end), type = "misc_feat"))
SeqIO.write(record, fo, "gb")