Thanks again
Just to clarify, I want to see the average price for year 2021, 2022 etc
based on the best fit. So naively if someone asked a question what the
average price will be in 2022, I should be able to make some predictions.
I can of course crudely use pen and pencil like shown in the attached
figure, but I was wondering if this is possible with anything that
matplotlib offers?
[image: Capture123.PNG]
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On Tue, 5 Jan 2021 at 15:22, Sean Owen <srowen@gmail.com> wrote:
> You will need to use matplotlib on the driver to plot in any event. If
> this is a single extrapolation, over 11 data points, you can just use Spark
> to do the aggregation, call .toPandas, and do whatever you want in the
> Python ecosystem to fit and plot that result.
>
> On Tue, Jan 5, 2021 at 9:18 AM Mich Talebzadeh <mich.talebzadeh@gmail.com>
> wrote:
>
>> thanks Sean.
>>
>> This is the gist of the case
>>
>> <https://stackoverflow.com/posts/65570917/timeline>
>>
>> I have data points for xaxis from 2010 till 2020 and values for y axis.
>> I am using PySpark, pandas and matplotlib. Data is read into PySpark from
>> the underlying database and a pandas Data Frame is built on it. Data is
>> aggregated over each year. However, the underlying prices are provided on a
>> monthly basis in CSV file which has been loaded into a Hive table
>>
>> summary_df = spark.sql(f"""SELECT cast(Year as int) as year,
>> AVGFlatPricePerYear, AVGTerracedPricePerYear, AVGSemiDetachedPricePerYear,
>> AVGDetachedPricePerYear FROM {v.DSDB}.yearlyhouseprices""")
>>
>> df_10 = summary_df.filter(col("year").between(f'{start_date}',
>> f'{end_date}'))
>>
>> p_dfm = df_10.toPandas() # converting spark DF to Pandas DF
>>
>>
>> for i in range(n):
>>
>> if p_dfm.columns[i] != 'year': # year is x axis in integer
>>
>> vcolumn = p_dfm.columns[i]
>>
>> print(vcolumn)
>>
>> params = model.guess(p_dfm[vcolumn], x = p_dfm['year'])
>>
>> result = model.fit(p_dfm[vcolumn], params, x = p_dfm['year'])
>>
>> result.plot_fit()
>>
>> if vcolumn == "AVGFlatPricePerYear":
>>
>> plt.xlabel("Year", fontdict=v.font)
>>
>> plt.ylabel("Flat house prices in millions/GBP", fontdict=v.font)
>>
>> plt.title(f"""Flat price fluctuations in {regionname} for the
>> past 10 years """, fontdict=v.font)
>>
>> plt.text(0.35,
>>
>> 0.45,
>>
>> "Bestfit based on NonLinear Lorentzian Model",
>>
>> transform=plt.gca().transAxes,
>>
>> color="grey",
>>
>> fontsize=10
>>
>> )
>>
>> print(result.fit_report())
>>
>> plt.xlim(left=2009)
>>
>> plt.xlim(right=2022)
>>
>> plt.show()
>>
>> plt.close()
>>
>> ```
>>
>> So far so good. I get a best fit plot as shown using Lorentzian model
>>
>> Also I have model fit data
>>
>> [[Model]]
>>
>> Model(lorentzian)
>>
>> [[Fit Statistics]]
>>
>> # fitting method = leastsq
>>
>> # function evals = 25
>>
>> # data points = 11
>>
>> # variables = 3
>>
>> chisquare = 8.4155e+09
>>
>> reduced chisquare = 1.0519e+09
>>
>> Akaike info crit = 231.009958
>>
>> Bayesian info crit = 232.203644
>>
>> [[Variables]]
>>
>> amplitude: 31107480.0 +/ 1471033.33 (4.73%) (init = 6106104)
>>
>> center: 2016.75722 +/ 0.18632315 (0.01%) (init = 2016.5)
>>
>> sigma: 8.37428353 +/ 0.45979189 (5.49%) (init = 3.5)
>>
>> fwhm: 16.7485671 +/ 0.91958379 (5.49%) == '2.0000000*sigma'
>>
>> height: 1182407.88 +/ 15681.8211 (1.33%) ==
>> '0.3183099*amplitude/max(2.220446049250313e16, sigma)'
>>
>> [[Correlations]] (unreported correlations are < 0.100)
>>
>> C(amplitude, sigma) = 0.977
>>
>> C(amplitude, center) = 0.644
>>
>> C(center, sigma) = 0.603
>>
>>
>> Now I need to predict the prices for years 20212022 based on this fit.
>> Is there any way I can use some plt functions to provide extrapolated
>> values for 2021 and beyond?
>>
>>
>> Thanks
>>
>>
>>
>>
>>
>> On Tue, 5 Jan 2021 at 14:43, Sean Owen <srowen@gmail.com> wrote:
>>
>>> If your data set is 11 points, surely this is not a distributed problem?
>>> or are you asking how to build tens of thousands of those projections in
>>> parallel?
>>>
>>> On Tue, Jan 5, 2021 at 6:04 AM Mich Talebzadeh <
>>> mich.talebzadeh@gmail.com> wrote:
>>>
>>>> Hi,
>>>>
>>>> I am not sure Spark forum is the correct avenue for this question.
>>>>
>>>> I am using PySpark with matplotlib to get the best fit for data using
>>>> the Lorentzian Model. This curve uses 20102020 data points (11 on xaxis).
>>>> I need to predict predict the prices for years 20212025 based on this
>>>> fit. So not sure if someone can advise me? If Ok, then I can post the
>>>> details
>>>>
>>>> Thanks
>>>>
>>>>
>>>>
>>>> LinkedIn * https://www.linkedin.com/profile/view?id=AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw
>>>> <https://www.linkedin.com/profile/view?id=AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw>*
>>>>
>>>>
>>>>
>>>>
>>>>
>>>> *Disclaimer:* Use it at your own risk. Any and all responsibility for
>>>> any loss, damage or destruction of data or any other property which may
>>>> arise from relying on this email's technical content is explicitly
>>>> disclaimed. The author will in no case be liable for any monetary damages
>>>> arising from such loss, damage or destruction.
>>>>
>>>>
>>>>
>>>
